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40 results about "Incremental learning algorithm" patented technology
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In DIL algorithm, incremental SVM is utilized as the base learner, while incremental learning is implemented by combining the existing base models with the ones generated on the new data. A novel weight update rule is proposed in DIL algorithm, being used to update the weights of the samples in each iteration.
Delivery vehicle scheduling method with time limitation
InactiveCN105069523APrevent falling intoEffective global searchForecastingLogisticsIncremental learning algorithmDelivery vehicle
The invention relates to a delivery vehicle scheduling method with time limitation, and belongs to the technical field of vehicle routing in intelligent optimized scheduling. A scheduling model and an optimization object in the delivery process with time limitation are determined, the optimization object is obtained by solving the scheduling model on the basis of an improved population incremental learning algorithm, the optimization object of the scheduling model is the minimal total transport cost, and the limitation condition is established according to time limitation of the delivery vehicle, load-carrying restriction, position coordinates of a client point and the like. Global searching is guided by a possibility matrix observation model, individual information in a historical group is optimized in the improved population incremental learning algorithm, global searching is effectively guided, and the efficiency of global searching is higher; global searching aimed at problem property is constructed based on limitation of the problem, a high-quality solution area obtained via global searching is rapidly and effectively searched, the performance of the algorithm is improved, and the optimal solution is obtained.
Owner:KUNMING UNIV OF SCI & TECH
Relevance vector regression incremental learning algorithm and system based on sample characteristics
ActiveCN106250988AImprove forecast accuracyReduce time complexityMachine learningIncremental learning algorithmReal-time data
The invention discloses a relevance vector regression incremental learning algorithm and system based on sample characteristics. The method comprises the following steps of: S1, obtaining an initial sample set, and initializing parameters; S2, training the sample set to obtain an RVM prediction model; S3, calculating a sample label, a local density factor and an error factor of each sample; S4, predicting a future sample to be input according to the RVM prediction model; S5, calculating sample characteristic vectors, arranging the sample characteristic vectors in a descending order, performing circulation, if the time of non-relevance vectors is beyond a set threshold value, deleting the sample from the sample set, and exiting the circulation; and S6, judging whether a new input sample exists or not, if so, adding a new sample to form a new sample set, turning to the step S2, and if not, outputting the predicted future sample. By means of the relevance vector regression incremental learning algorithm and system disclosed by the invention, a sample including effective information can be reserved; an invalid sample can be deleted; the prediction precision is relatively high; the time complexity is relatively low; and thus, the relevance vector regression incremental learning algorithm and system can be widely applied in real-time data processing and prediction.
Owner:WUHAN UNIV OF TECH
Scheme for mining abnormal behaviour of industrial internet
InactiveCN106230772AImprove robustnessCalculation speedTransmissionIncremental learning algorithmClassification methods
According to data behaviour characteristics in an industrial control network in a mobile internet environment, the invention provides an improved hybrid multi-classification naive Bayesian algorithm and a mass data incremental learning algorithm based on two-step screening, and is applied to abnormal behaviour mining and analysis of a mobile industrial control system. An abnormal behaviour mining scheme is formed by abnormal behaviour classification and mining algorithm design: a suspicious behaviour classification method and a data mining process are included, and in the industrial control network in the mobile internet environment, behaviour data mining is divided into two stages including a classifier learning stage and a network behaviour monitoring stage; and, after all kinds of behaviour classifiers are obtained, malicious software behaviour data mining enters the second stage, namely the network behaviour monitoring stage. On the premise that classification categories are independent, the naive Bayesian classification algorithm has the characteristics of high calculation speed and classification accuracy, good robustness and the like, and are widely applied.
Owner:STATE GRID QINGHAI ELECTRIC POWER +1
Face recognition method based on improved incremental non-negative matrix factorization
ActiveCN108846357AShort training timeEfficient use ofCharacter and pattern recognitionIncremental learning algorithmMatrix solution
The invention discloses a face recognition method based on improved incremental non-negative matrix factorization. An incremental non-negative matrix solution algorithm based on Fisher discriminant analysis is adopted; prior information trained by an initial sample is utilized in the algorithm; the column vector of a newly increased coefficient is assigned to a corresponding means vector through an index matrix; therefore, the convergence speed of the algorithm is increased; in addition, an INMF algorithm is improved into a batch incremental learning algorithm; initialized assignment is carried out by utilization of the index matrix; furthermore, restriction having the maximum interclass divergence and the minimum in-degree is imposed onto a target function; and thus, the best sub-space projection is obtained. The scheme has relatively high recognition rate and relatively rapid recognition speed, and is applied to occasions, such as a face recognition scene.
Owner:杭州金视线科技有限公司
Rolling bearing fault detection method based on actual measurement signal
PendingCN112414713AApparently non-linear non-stationaryImprove the accuracy of fault identificationMachine part testingIncremental learning algorithmIncremental learning
The invention discloses a rolling bearing fault detection method based on an actual measurement signal. The rolling bearing fault detection method comprises the steps of: firstly, converting a rollingbearing fault time domain vibration signal to an angle domain through employing an order tracking technology; secondly, carrying out parameter optimization on variational mode decomposition through adpopting a longicorn beard search algorithm, and decomposing all state vibration signals of a rolling bearing to obtain a series of intrinsic mode functions, wherein frequency band energy in differentintrinsic mode functions can change when different faults happen to the bearing; thirdly, extracting Renyi entropy features from modal components containing main fault information, and constructing afeature subset; and finally, using normal state vibration signals easy to obtain for training, extracting fault characteristic quantities, establishing fault data samples and incremental learning data samples, acquiring a fault recognition model through training by adopting a single-class support vector machine incremental learning algorithm, judging whether the rolling bearing breaks down or notaccurately, and achieving fault early warning.
Owner:吉电(滁州)章广风力发电有限公司 +1
Virtualized network function placement method based on population-based incremental learning algorithm
InactiveCN107749801AGood optimization resultReduce latencyData switching networksIncremental learning algorithmAlgorithms performance
The invention discloses a virtualized network function placement method based on a population-based incremental learning algorithm. The population-based incremental learning algorithm is applied to apreset network topology, minimization of service delay is taken as an optimization target, and a virtualized network function placement scheme satisfying a practical deployment demand is calculated. The method is mainly characterized by that a two-stage coding mode is employed, reparation operation is carried out on illegal individuals, a Floyd algorithm is employed when individual fitness is calculated, a probability vector is guided to be updated through storage of a global elite set, the probability vector is changed through mutation operation, and the like. According to the method, the PBIL (Population-Based Incremental Learning) algorithm is applied to solution of the VNF-P (Virtualized Network Functions Placement) algorithm; simulation experiment and data analysis show that comparedwith a genetic algorithm, the method provided by the invention has the remarkable advantages of algorithm performance and efficiency; a solution enabling service delay to be relatively small can be obtained; and the feasibility and high efficiency of the method are proved.
Owner:SOUTHWEST JIAOTONG UNIV
sEMG self-adaptive mode recognition method based on on-line SVM and application of method on intelligent wheelchair
InactiveCN104463218AAvoid fatigueProtection against transient signalsCharacter and pattern recognitionPattern recognitionIncremental learning algorithm
The invention relates to an sEMG self-adaptive mode recognition method based on an on-line SVM and application of the method on an intelligent wheelchair and belongs to the technical field of surface electromyogram signal recognition and control. According to the method, the incremental learning algorithm is adopted to perform on-line training on an SVM model, and meanwhile head information is introduced in the on-line learning process and serves as correction information to jointly form a classifier of an sEMG; effective recognition is achieved on the sEMG at different muscle states, influences on the stability of a man-machine interactive system by muscle fatigue are reduced, and finally the method is successfully applied to the intelligent wheelchair. The method effectively improves the self-adaptive capacity of the system in the long-term man-machine interaction process, and the interaction can be more natural and friendlier.
Owner:CHONGQING UNIV OF POSTS & TELECOMM
Compressor outlet parameter prediction modeling method based on width learning system
PendingCN110222453AImprove modeling efficiencyRefactoring is cheapDesign optimisation/simulationSpecial data processing applicationsIncremental learning algorithmRoot-mean-square deviation
The invention discloses a compressor outlet parameter prediction modeling method based on a width learning system. The method comprises the steps of collecting data and preprocessing the data; constructing an outlet parameter prediction model of the compressor by utilizing a width learning system, setting the number of characteristic node groups of the width learning system as n, the number of k characteristic nodes in each group, the number of enhanced node groups as m and q enhanced nodes in each group, and performing modeling; when a new training data input model exists, using an incremental learning algorithm for carrying out online updating on an original model, an input data matrix of new training data being set as Xa belonging to Ra * M, an output data matrix being set as Ya belonging to Ra * C, a representing the number of newly-added training data, and performing model updating; and verifying the constructed model by using the root-mean-square error. According to the method, the performance prediction model of the large compressor can be quickly established, and the development time and cost of the model can be effectively saved.
Owner:CHINA UNIV OF MINING & TECH
Representative data reconstruction-based incremental SVR (support vector regression) load prediction method
ActiveCN105809286ASettings updateSmall modeling complexityForecastingIncremental learning algorithmPredictive methods
The invention discloses a representative data reconstruction-based incremental SVR (support vector regression) load prediction method. The method includes the following steps that: electric load data are acquired; multiple-input-single-output pattern data are obtained through using a phase-space reconstruction theory; a support vector regression model is established by using the obtained pattern data and a particle swarm algorithm; newly-increased electric power load prediction data are obtained in real time; an optimal representative data subset is updated through using an incremental learning algorithm; model parameters are updated by using a nested particle swarm method; a support vector regression model is established by using the updated model parameters and optimal the representative data subset; and incremental load prediction is determined, and an incremental load prediction value is outputted. According to the method of the invention, support vectors of support vector regression are applied to the knowledge understanding research of massive data. With the method adopted, newly increased data-caused representative data reconstruction can be realized; the problems of high calculation complexity of massive data and difficulty in knowledge extraction can be effectively solved; the updating of the model parameters is realized in a nested manner; and references can be provided for the planning and operation of an electric power system.
Owner:NANCHANG INST OF TECH
Optimal scheduling method for multi-objective optimization military transportation process
InactiveCN104504453AImproved local development capabilitiesQuality improvementForecastingIncremental learning algorithmAlgorithm
The invention relates to an optimal scheduling method for a multi-objective optimization military transportation process, and belongs to the technical field of intelligent optimal scheduling of vehicle scheduling. A scheduling model and an optimization objective of the multi-objective optimization military transportation process are determined, and an optimal scheduling method based on a quantum population incremental learning algorithm is used, so that the optimization objective is optimized. The scheduling model and the optimization objective of the multi-objective optimization military transportation process are put forward, and the structure is clear and accurate. An optimal individual of a current population is obtained according to algorithm steps, and information about the optimal individual is fed back to a quantum bit observation model for updating a next generation population, so that global search through the algorithm can be effectively guided. The operation of Ejection Pool and Operate Assemble is executed on the optimal individual of the population, so that the local development capability of the algorithm is remarkably enhanced, and the solution quality is further enhanced.
Owner:KUNMING UNIV OF SCI & TECH
A knowledge base construction and partial order structure graph generation method based on incremental learning
ActiveCN109376248AEnabling incremental buildsRealize dynamic constructionSemantic tool creationIncremental learning algorithmAlgorithm
The invention discloses a knowledge base construction and partial order structure graph generation method based on incremental learning. The incremental learning idea based on the covering principle is adopted to guide the covering operation of attribute and object set, and the special set such as attribute database is constructed by special covering relation, and then the formal background is generated, and the redundant pattern is deleted. On the basis of formal background, the algorithm of computer generation is to get the precise coordinate location of each concept node by computing the hierarchical pattern matrix, and to complete the dynamic construction of partial order structure graph by synthesizing the elements of hierarchy, node, line and so on, and introducing incremental learning algorithm. Compared with the prior art, the invention combines the incremental learning based on the covering principle and the construction algorithm of the partial order structure graph to realize the dynamic construction of the knowledge concept base and the automatic generation of the hierarchical pattern matrix. The progressive construction of partial order structure graph is realized.
Owner:梁怀新 +2
Method for controlling standby restart of centrifugal-type water cooling unit
ActiveCN104567162AGuaranteed uptimeReduce hidden dangersRefrigeration safety arrangementCentrifugal chillerIncremental learning algorithm
The invention provides a method for controlling standby restart of a centrifugal-type water cooling unit. The method includes step S1, acquiring running parameters before standby, and comparing the running parameters with preset parameters to determine a standby reason; step S2, when the standby reason is water temperature standby, setting restart target parameters according to an incremental learning algorithm; step S3, controlling restart of the unit according to the restart target parameters. According to the method, when the standby reason is water temperature standby, the restart target parameters are set according to the incremental learning algorithm, and restart of the unit is controlled according to the restart target parameters, so that running load of the unit is enabled to be matched with working conditions, the unit is enabled to run under small-load working conditions, potential safety hazards caused by frequent start and stop of the unit are lowered, and the unit is enabled to be more energy saving. In addition, high-low pressure difference of the unit can be lowered, so that the unit is enabled to be away from a surge area, and potential surge possibility of the unit is lowered.
Owner:GREE ELECTRIC APPLIANCES INC +1
Logistics customer loss prediction method and system, and medium
ActiveCN108921342AImprove fault toleranceImprove the speed of predictionForecastingNeural architecturesFault toleranceHidden layer
The invention discloses a logistics customer loss prediction method and system, which has high fault tolerance, accuracy and timeliness, is rapid and reliable in prediction, and is suitable for predicting customer loss of dynamic data flow. According to the technical scheme, the method comprises the steps of collecting logistics industry customer behavior preference big data; extracting conditionfactors with relatively strong correlation with the customer loss from the collected logistics industry customer behavior preference big data by adopting a rough set theory, thereby forming an original decision table; performing information entropy-based discretization processing on continuous attribute values in the formed original decision table to obtain an initial decision table; reducing redundant condition attributes in the initial decision table to obtain a secondary decision table, which serves as an input of a BP neural network; and by using an incremental learning algorithm, adaptively determining the number of neurons in a hidden layer by using an ELM as a basic classifier, and verifying the accuracy of the decision table; and adjusting a weight and a threshold value of an output layer, and training a data set until an optimal solution is output.
Owner:YTO EXPRESS CO LTD
Face recognition tracker based on incremental learning algorithm
InactiveCN111027476AImprove recognition rateHigh precisionCharacter and pattern recognitionIncremental learning algorithmVisual perception
The invention discloses a face recognition tracker based on Haar-like features and an incremental learning algorithm, and mainly relates to the field of computer vision and image processing. Accordingto the method, Haar-like feature evaluation is accelerated by using an integral graph, strong classifiers for distinguishing human faces and non-human faces are trained by using an AdaBoost algorithm, and the strong classifiers are cascaded together by using screening type cascading, so that the accuracy is improved. And the face tracking part predicts the position of the central point of the current time frame according to the position of the central point of the previous frame of image tracking frame. And main features of the image in the frame are extracted by using a PCA algorithm, and acorresponding dimension-reduced graph is predicted according to the position of the center point of the frame at the moment. A forgetting factor is introduced, and image data is updated once every five frames. The incremental algorithm does not need to train a model, so that the efficiency is improved. Theoretics and practices show that the method can automatically recognize a human face, when thedirection of the human face changes greatly, for example, when the front face becomes a side face, recognition and tracking can be continued, continuous recognition is kept, and interruption is avoided.
Owner:UNIV OF ELECTRONICS SCI & TECH OF CHINA
Voiceprint recognition method based on negative correlation incremental learning
InactiveCN107154258AImprove recognition accuracySolve the problem of incremental learningSpeech recognitionLearning basedIncremental learning algorithm
The invention provides a voiceprint recognition method based on negative correlation incremental learning. The method comprises: step one, preprocessing and feature extraction are carried out on an inputted voice signal; step two, network integration is initialized; and if network integration already exists, current all networks are copied; step three, the network integration is trained; step four, structure adjustment is carried out on each network in the network integration; step five, the current networks are screened and the part of optimal networks are selected; and step six, the currently obtained networks are applied; and if new data arrive, steps are executed circularly by starting with the step one. According to the voiceprint recognition method provided by the invention, with the incremental learning method, voiceprint recognition is studied, so that the efficiency and identification accuracy under the data incremental arrival scene can be improved; and an incremental problem can be solved by using the negative-correlation-learning-based incremental learning algorithm. Improvement is carried out from perspectives of model training and model selection and thus a novel algorithm is put forward to solve problems; and then the novel method is applied to incremental learning.
Owner:HARBIN ENG UNIV
Annotation system and method suitable for substation equipment inspection images
ActiveCN112102443AEfficient and accurate labelingClosely connectedGeometric image transformationNeural architecturesManual annotationData set
The invention discloses an annotation system suitable for substation equipment inspection images. The system comprises an application module and an annotation model, wherein the application module comprises three functional modules including an import module, a regional operation module and a detection and analysis module; the annotation model comprises four parts including an incremental learningalgorithm, a semi-automatic annotation strategy, an annotation functional module and an annotation process design. According to the method, efficient and accurate annotation of a large number of inspection images generated by a transformer substation scene every day can be achieved, and compared with a time-consuming and labor-consuming manual annotation method and a low-accuracy full-automatic annotation method, the annotation time can be greatly shortened, and extremely high accuracy is achieved. Further, the work of data set acquisition, classification, annotation and the like can be closely linked, and various work systems are efficiently and clearly displayed in front of an operator, so that the work efficiency is remarkably improved.
Owner:WUHAN NARI LIABILITY OF STATE GRID ELECTRIC POWER RES INST +2
Online abnormality detection method for transformer vibration
ActiveCN109029699AEasy to handleQuick upgradeSubsonic/sonic/ultrasonic wave measurementIncremental learning algorithmTime segment
In view of how to analyze the operating state of a transformer and perform fault diagnosis based on a vibration method, the embodiment of the invention discloses an online abnormality detection methodfor transformer vibration. The method comprises: obtaining new data samples about a vibration signal of the transformer operating state within a preset time period; extracting the characteristic parameters of the new data samples based on wavelet packet analysis; completing the learning of the new data samples based on a fast convex hull algorithm, training and updating a single-class abnormalitydetector model; and calling the updated single-class abnormality detector model to perform online abnormality detection of the current transformer vibration. According to the online abnormality detection method for transformer vibration, since the incremental learning algorithm is used to learn only the new data samples within the preset time period, the new data can be processed in time and effectively, the new training samples can be learned online in real time, and rapid upgrade of detection models can be achieved. At the same time, time and space requirements of the model update can be reduced.
Owner:LESHAN POWER SUPPLY COMPANY STATE GRID SICHUAN ELECTRIC POWER +1
A mobile application malware detection method and system for a power enterprise
PendingCN109684837AClassification accuracy does not dropShorten study timeDecompilation/disassemblyData processing applicationsFeature vectorIncremental learning algorithm
The mobile application malicious software detection method for the power enterprise is characterized by comprising the steps that to-be-detected software is acquired, and decompilation is conducted onthe to-be-detected software to obtain a source code of the to-be-detected software; Extracting a feature vector of the source code, inputting the feature vector of the source code into a pre-constructed SVM classification model for comparison, and determining whether the to-be-detected software is malicious software or not; Wherein the SVM classification model comprises an SVM classifier, and theSVM classifier performs iterative updating based on a double weight increment method. According to the technical scheme, the problem that a support vector machine is not suitable for classified learning of a large number of samples is solved, the SVM incremental learning algorithm based on the double-weight function is provided for learning and classifying the application samples, and the methodcan reduce the learning time to the maximum extent and improve the learning efficiency on the premise that it is guaranteed that the application classification precision is not reduced.
Owner:GLOBAL ENERGY INTERCONNECTION RES INST CO LTD +2
Method and device for training risk identification model
InactiveCN113191434AImprove training efficiencySave computing resourcesCharacter and pattern recognitionProtocol authorisationIncremental learning algorithmEngineering
The embodiment of the invention provides a method and device for training a risk identification model. According to the method of the embodiment, firstly, determining a newly-added task; then determining the similarity between the newly added task and an existing task corresponding to a first risk identification model obtained by training; if the similarity meets a preset condition, modifying a structure in the first risk identification model according to the newly added task, the modified structure comprising a newly added structure and / or a deleted structure; and finally, updating model parameters of the first risk identification model containing the modified structure by using the newly added task by adopting an incremental learning algorithm to obtain a second risk identification model.
Owner:ALIPAY (HANGZHOU) INFORMATION TECH CO LTD
Intelligent warehousing sorting method and system based on incremental learning
InactiveCN113837156AImprove recognition accuracyImprove recognition efficiencyCharacter and pattern recognitionNeural learning methodsIncremental learning algorithmData set
The invention relates to an intelligent warehousing sorting method and system based on incremental learning. The method comprises the following steps: identifying object types of storage object images in a storage object sample image set by adopting an open set identification algorithm; when the storage object image is a newly-added storage object image, generating a newly-added storage object image data set; optimizing the warehousing system classification model to obtain an incremental learning algorithm model, wherein the warehousing system classification model is a warehousing object classification model originally implanted in the intelligent warehousing system; training an incremental learning algorithm model by adopting a newly added storage object image data set; and inputting the to-be-detected storage object image to the trained incremental learning algorithm model to obtain the object type of the to-be-detected storage object. According to the invention, the incremental learning model is applied to the intelligent warehousing system, so that the recognition accuracy of old-class warehousing objects can be improved, and the recognition efficiency of old-class and new-class warehousing objects through autonomous learning can be improved.
Owner:北京中超伟业信息安全技术股份有限公司
Vehicle sending information authenticity real-time detection method based on width learning
ActiveCN110213741ASatisfy real-timeAccuracy meetsParticular environment based servicesVehicle-to-vehicle communicationIncremental learning algorithmFeature extraction
The invention discloses a vehicle sending information authenticity real-time detection method based on width learning. The vehicle sending information authenticity real-time detection method comprisesthe following steps: (1) collecting and sharing vehicle data information; (2) performing feature extraction on the acquired information, standardizing the proposed features, and standardizing the authenticity label of the message by using a two-dimensional vector; (3) establishing a false message detection model; (4) training a false message detection model; (5) detecting the authenticity of themessage sent by the vehicle; and (6) updating the model: when new data collected in the system is accumulated to a certain quantity, triggering the model to automatically utilize the newly added datato update by using an incremental learning algorithm. According to the method disclosed by the invention, the requirements on real-time performance and expandability of improper behavior detection ofthe vehicle can be well met. Meanwhile, the resource consumption is reduced, a more reliable basis is provided for traffic safety analysis, and potential safety hazards in traffic are reduced.
Owner:QINGDAO ACADEMY OF INTELLIGENT IND +1
Incremental width and deep learning drug response prediction method, medium and equipment
PendingCN114841261AImprove performanceImprove accuracyDrug and medicationsCharacter and pattern recognitionIncremental learning algorithmMedication information
The invention provides a drug response prediction method based on incremental width and deep learning, a medium and equipment. The method comprises the following steps: performing text coding and position coding on a drug sequence to construct a drug information code; inputting the drug information code into a Transform encoder to excavate the structural features of the drug, inputting gene expression data into the feature representation of a multilayer perceptron learning gene, and splicing the drug features and the gene features together to form a drug-gene feature pair; and inputting the feature pair into a width learning system to obtain a predicted drug sensitivity regression value. The method can solve the problem of poor drug expression; drug expression and gene expression characteristics are fused by adopting a width learning system, so that the accuracy of a drug sensitivity prediction result is improved; the network weight is updated through an incremental learning algorithm, the model performance is improved, and the whole model does not need to be retrained.
Owner:SOUTH CHINA UNIV OF TECH
Writer self-adapting method based on increment quadratic discriminant function for discriminating subspace
ActiveCN102184425AImprove recognition accuracyRecognition accuracy remains unchangedCharacter and pattern recognitionHandwritingIncremental learning algorithm
The invention provides writer self-adapting method based on an increment quadratic discriminant function for a discriminating subspace, and the method comprises the following steps: dynamically updating an MQDF (modified quadratic discriminant function) recognition model by utilizing an increment sample of the writing style of a special user; and leading the updated MQDF recognition model to adapt to the writing style of the special user, thus achieving the effect of improving the recognition rate of the special user. By creatively combining an increment learning algorithm, and an MQDF sorting algorithm and an ILDA (Increment Linear Discriminant Analysis) algorithm, the invention provides an ILDAIMQDF algorithm, and applies the ILDAIMQDF in the field of self-adaption of writers based on handwritten Chinese character recognition, thus solving the problem that the handwritten Chinese character recognition engine has low recognition rate on the special user. By utilizing the method, the handwritten Chinese character recognition engine can adapt to the handwriting style of the specific user, so as to improve the recognition accuracy.
Owner:SOUTH CHINA UNIV OF TECH
Lithium battery temperature field online modeling method and system
PendingCN114692494AGuaranteed timelinessQuick updateDesign optimisation/simulationComplex mathematical operationsIncremental learning algorithmOnline model
The invention discloses a lithium battery temperature field online modeling method and system. The method comprises the following steps: firstly, carrying out space-time separation on space-time data by using a KL method to respectively obtain a space basis function and a time coefficient; secondly, training a time coefficient by using a width learning network to obtain an offline model of the lithium battery temperature field; and then updating the network weight of the off-line model by using an incremental learning algorithm of width learning, establishing an on-line model of the lithium battery temperature field, and finally reconstructing an obtained prediction time coefficient and a space basis function to obtain a final prediction model to predict data. According to the method, modeling is carried out on the temperature field of the lithium ion battery, and online updating is carried out on the model by using an incremental learning algorithm with excellent width learning, so that the model adapts to the time-varying behavior of the system, and the problem of temperature field prediction with unknown, nonlinear and time-varying characteristics of the system is solved.
Owner:GUANGDONG UNIV OF TECH
Centrifugal chiller standby restart control method
ActiveCN104567162BGuaranteed uptimeReduce hidden dangersRefrigeration safety arrangementCentrifugal chillerIncremental learning algorithm
Owner:GREE ELECTRIC APPLIANCES INC +1
An Online Abnormal Detection Method for Transformer Vibration
ActiveCN109029699BEasy to handleQuick upgradeSubsonic/sonic/ultrasonic wave measurementIncremental learning algorithmAlgorithm
In view of how to analyze the operating state of a transformer and perform fault diagnosis based on a vibration method, the embodiment of the invention discloses an online abnormality detection methodfor transformer vibration. The method comprises: obtaining new data samples about a vibration signal of the transformer operating state within a preset time period; extracting the characteristic parameters of the new data samples based on wavelet packet analysis; completing the learning of the new data samples based on a fast convex hull algorithm, training and updating a single-class abnormalitydetector model; and calling the updated single-class abnormality detector model to perform online abnormality detection of the current transformer vibration. According to the online abnormality detection method for transformer vibration, since the incremental learning algorithm is used to learn only the new data samples within the preset time period, the new data can be processed in time and effectively, the new training samples can be learned online in real time, and rapid upgrade of detection models can be achieved. At the same time, time and space requirements of the model update can be reduced.
Owner:LESHAN POWER SUPPLY COMPANY STATE GRID SICHUAN ELECTRIC POWER +1
A Face Recognition Method Based on Improved Incremental Non-negative Matrix Factorization
ActiveCN108846357BShort training timeEfficient use ofCharacter and pattern recognitionPattern recognitionIncremental learning algorithm
The invention discloses a face recognition method based on improved incremental non-negative matrix decomposition, which adopts an incremental non-negative matrix solution algorithm based on Fisher discriminant analysis. The algorithm uses the prior information of the initial sample training, assigns the column vector of the new coefficient to the mean vector of the corresponding category through the index matrix, and improves the convergence speed of the algorithm; in addition, the INMF algorithm is improved to a batch incremental learning algorithm , use the index matrix for initialization assignment, and impose the constraints of maximum inter-class scatter and minimum in-degree on the objective function, so as to obtain the best subspace projection. This solution has a higher recognition rate and a faster recognition speed, and is suitable for occasions such as face recognition.
Owner:杭州金视线科技有限公司
Crop disease and pest identification method and device based on incremental learning and storage medium
InactiveCN114283308AGuaranteed growth environmentAccurate detectionCharacter and pattern recognitionNeural architecturesPattern recognitionIncremental learning algorithm
The invention relates to a crop disease and pest identification method and device based on incremental learning and a storage medium, and the method comprises the steps: obtaining the existing disease and pest image data which comprise the known disease and pest image data; preprocessing the known disease-like pest image data, and adding a first disease-like pest category label to the known disease-like pest image data to obtain a known disease-like pest sample set; taking the known disease and pest sample set as input, and training a neural network by using a cross entropy loss function to obtain a standard disease and pest image classification model; modifying a structure in the standard disease and pest image classification model by adopting an incremental learning algorithm, and modifying a total loss function in the incremental learning algorithm by adopting a dynamic parameter correction method to obtain an incremental disease and pest image classification model; and inputting the obtained new disease and pest image data into the incremental disease and pest image classification model for identification and classification. According to the method, new disease and pest categories can be accurately detected, and the identification result is more accurate.
Owner:HKUST TIANGONG INTELLIGENT EQUIP TECH (TIANJIN) CO LTD
Knowledge Base Construction and Partial Order Structure Graph Generation Method Based on Incremental Learning
ActiveCN109376248BEnabling incremental buildsRealize dynamic constructionSemantic tool creationIncremental learning algorithmAlgorithm
The invention discloses a method for constructing a knowledge base based on incremental learning and generating a partial sequence structure graph. It adopts the idea of incremental learning based on the covering principle to guide the covering operation of attributes and object sets, and completes special functions such as attribute libraries through special covering relations. The construction of the set further completes the generation of the formal background, and completes the deletion of redundant patterns; it is constructed on the basis of the formal background, and its computer generation algorithm obtains the precise coordinate positioning of each concept node by calculating the hierarchical pattern matrix. Layers, nodes, connections and other elements, as well as the introduction of incremental learning algorithms, complete the dynamic construction of partial order structure graphs. Compared with the prior art, the present invention combines the incremental learning based on the coverage principle and the construction algorithm of the partial order structure graph, which can realize the dynamic construction of the knowledge concept base and complete the automatic generation of the hierarchical pattern matrix; realize the partial order structure Incremental construction of graphs.
Owner:梁怀新 +2
A Virtual Network Function Placement Method Based on Population Incremental Learning Algorithm
InactiveCN107749801BGood optimization resultReduce latencyData switching networksIncremental learning algorithmAlgorithm
The invention discloses a virtualized network function placement method based on a population-based incremental learning algorithm. The population-based incremental learning algorithm is applied to apreset network topology, minimization of service delay is taken as an optimization target, and a virtualized network function placement scheme satisfying a practical deployment demand is calculated. The method is mainly characterized by that a two-stage coding mode is employed, reparation operation is carried out on illegal individuals, a Floyd algorithm is employed when individual fitness is calculated, a probability vector is guided to be updated through storage of a global elite set, the probability vector is changed through mutation operation, and the like. According to the method, the PBIL (Population-Based Incremental Learning) algorithm is applied to solution of the VNF-P (Virtualized Network Functions Placement) algorithm; simulation experiment and data analysis show that comparedwith a genetic algorithm, the method provided by the invention has the remarkable advantages of algorithm performance and efficiency; a solution enabling service delay to be relatively small can be obtained; and the feasibility and high efficiency of the method are proved.
Owner:SOUTHWEST JIAOTONG UNIV
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