31 results about "Gaussian membership function" patented technology

The invention relates to a self-learning mechanism-base fast matching fuzzy reasoning method. The method includes the following steps that: a Gaussian membership degree function method is adopted to construct parameter fuzzification information; a fuzzy rule base is established; external parameters are fuzzificated, so that a fact item can be obtained; the fact item is matched with rules in the fuzzy rule base by adopting a rete algorithm, so that a fuzzy reasoning result can be obtained; the fuzzy reasoning result is subjected to defuzzification, so that a final reasoning result can be obtained; and a sample set is constructed according to the final reasoning result and an actual feedback result, and rule strength self-learning correction is carried out based on the sample set. According to the self-learning mechanism-base fast matching fuzzy reasoning method of the invention, the rete algorithm is adopted, so that the efficiency of fuzzy reasoning can be improved, and the fuzzy reasoning method can be applied to the engineering field with high real-time requirements.

The invention discloses an acoustic-wave-method gas pipeline leakage monitoring method based on ANN (Artificial Neural Network). The acoustic-wave-method gas pipeline leakage monitoring method comprises the following steps that sound wave signal samples on multiple working conditions of a gas pipeline are acquired, the sound wave signal samples on the different working conditions are denoised and characteristic values of the sound wave signal samples on the different working conditions are extracted; the Gaussian membership function is adopted to conduct fuzzy segmentation on the sound wave signal samples on the different working conditions to acquire fuzzy segmentation amount; the F-self-adaption genetic algorithm is used for optimizing the initial training value of the BP (Back Propagation) neural network, the fuzzy segmentation amount is substituted into the BP neural network for training, and a gas pipeline instant working condition judging BP neural network is acquired; gas pipeline working conditions are judged with the output value of the BP neural network according to the principle of proximity and the time-phased statistical method, and the leakage position is confirmed through the cross-correlation function when leakage occurs. The acoustic-wave-method gas pipeline leakage monitoring method has the advantages that the leakage recognition accuracy rate is increased and the leakage judging reliability degree is increased due to the fact that the principle of proximity and the time-phased statistical method are adopted to determine the working condition states of pipelines.

The invention discloses a comprehensive intelligent method for predicting an impact danger level based on micro-seismic fractal, and is applicable to the field of mine pressure impact prediction in the mine safety field. The method comprises three parts of micro-seismic fractal index input, fuzzy intelligent processing analysis and intelligent prediction result output. The method comprises specific steps of calculating micro-seismic time, space and energy fractal indexes, establishing an impact danger level membership matrix, establishing an index abnormity index and a Gaussian membership function, establishing a single-index evaluation matrix R, and calculating and updating each index weight matrix in real time by adopting an F value scoring method in a confusion matrix; calculating the membership probability of each impact danger level, and determining the final impact danger level by combining a maximum membership degree principle (MMDP) and a variable fuzzy feature recognition model (VFPR). The method is clear in mathematical model, high in universality and operability, capable of achieving intelligent probability prediction of different impact danger levels and intelligent recognition of comprehensive results, good in application feasibility and high in intelligent prediction efficiency.

The invention discloses a fuzzy fusion-based multi-sensor crack damage comprehensive diagnosis method. The method comprises the steps of A, collecting a fiber grating spectrum, a Lamb signal and an intelligent coating resistance value; B, extracting characteristic parameters; C, respectively performing fuzzification processing on characteristic parameters of signals collected by fiber grating, piezoelectric and intelligent coating sensors by use of a Gaussian membership function to obtain three membership functions; D, performing fuzzy fusion by use of a Jaeger algorithm to obtain a comprehensive membership function and a fusion factor omega; E, defuzzifying the comprehensive membership function obtained by fuzzy fusion to obtain fused characteristic parameters; and F, performing relationfitting processing on data obtained by fuzzification processing by use of the fusion factor and fiber grating, piezoelectric and intelligent coating sensor data, and obtaining a relation between the fused characteristic parameters and a damage amount by prediction and verification. According to the fuzzy fusion-based multi-sensor crack damage comprehensive diagnosis method, the problem that information judgment and comprehensive diagnosis cannot be directly performed by use of the multi-sensor data at present can be effectively solved.

The invention relates to a palm vein fusion feature recognition method. According to the palm vein fusion feature recognition method, the vein line features of four finger areas and one palm area are extracted to be matched, and the identity of a user is discriminated through fusion of the matching result of the five areas so that reliable identity authentication can be realized. The finger vein features of the four fingers and the palm vein features of one palm area are fused so that the feature discrimination capacity can be enhanced and the security of feature recognition can be enhanced; meanwhile, the vein feature matching result of the five areas is fused by using a Gaussian membership function so that the robustness of a feature matching algorithm can be enhanced. The relevant data can be acquired in one step by using the same set of vein acquisition equipment, and the feature discrimination capability can be enhanced and the security performance of identity authentication can be enhanced under the condition that the equipment cost and the acquisition convenience are basically the same so that the palm vein fusion feature recognition method can be widely applied to intelligent entrance guard systems.

The invention discloses a fuzzy logic and evidence reasoning-based transformer insulation stress calculation and evaluation method. The method comprises the following steps of firstly normalizing field data, namely, converting input data for describing a normal value, a limit value and the like of a transformer into dimensionless variables of a [0,1] region; secondly fuzzifying the input data, namely, mapping the input data to a fuzzy membership function, wherein a trapezoidal membership function, a generalized bell-like membership function or a Gaussian membership function can be used; thirdly building a transformer stress evaluation tree model, namely, establishing nodes from the input data of the transformer to an insulation stress of the transformer, and a connection mode of the nodes, and correlating the nodes of upper and lower layers by using a fuzzy correlation matrix; fourthly determining a weight value between the nodes, namely, obtaining a synthesized weight value by using a subjective weight value of an analytic hierarchy process and an objective weight value of eigenvector analysis; and finally obtaining the insulation stress of the transformer, namely, determining a total stress level of the transformer by using a reasoning mechanism and an evidence synthesis rule. The method has the advantages in accuracy, flexibility and capability of processing measurement uncertainty.

The invention provides a robust adaptive fault reconstruction method of a microturbine. The method comprises that a nonlinear part level model of the microturbine is established, a TS fuzzy mathematical method is used to describe a nonlinear system mathematically, and an optimization method is used to optimize a Gaussian-like membership function used in a TS fuzzy system so as to improve the precision of the mathematical model; a state estimation based sliding form observer method is used to reconstruct a fault; and when a nonsingular terminal sliding form observer is designed, linear transformation is introduced into the TS fuzzy system to separate a fault signal from an interference signal, applied interference and a fault signal whose upper bounds of the change rate are not known can be reconstructed simultaneously, and an adaptive law is introduced to update the sliding form gain in real time to eliminate influence of the fault and interference with unknown upper bound of the change rate on sliding form motion. According to experimental results, the method is robust to the fault and interference with unknown upper bound of the change rate, and the unknown fault can be reconstructed in extremely short time.

The invention relates to an evaluation method for open, fair and impartial dispatching power generation schedule. The method comprises the following steps: 1) establishing an evaluation index system and an evaluation set of the power generation schedule, and obtaining evaluation index values of different schemes; 2) obtaining comprehensive weight vector according to the evaluation index system by utilizing an improved fuzzy analytical hierarchy process; 3) obtaining first fuzzy comprehensive evaluation result vectors of the different schemes according to the evaluation set and the evaluation index values by utilizing an isosceles triangle membership function; 4) obtaining second fuzzy comprehensive evaluation result vectors of the different schemes according to the evaluation set and the evaluation index values by utilizing a Gauss membership function; and 5) obtaining the optimum fuzzy comprehensive evaluation result vector of the different schemes by combining the first fuzzy comprehensive evaluation result vectors and the second fuzzy comprehensive evaluation result vectors based on vector similarity. Compared with the prior art, the method has the advantages of eliminating the difference between signal methods, and improving feasibility of evaluation result and the like.

The invention belongs to the field of image processing and provides an image description method and system based on a bag-of-words model. According to the method and system, the fuzzy theory is applied on the basis of the existing technologies, and different membership degrees are distributed to all visual words through a Gaussian membership function after a distance set is obtained, so that the distance set is converted into a fuzzy set; afterwards, feature points are coded by means of the visual words with the different membership degrees. Thus, the problem of information loss caused by a traditional method is effectively avoided, and then the accuracy of image description is improved.

The invention presents a fuzzy basis function network (FBFN) processing device for the beam pointing error correction of the local multipoint distributed system (LMDS). The beam pointing error caused by wind force can affect the performance of the local multipoint distributed system (LMDS). The correction device uses multi-beam planar array antenna to obtain the signal direction-of-arrival (DOA) of a base station and uses fuzzy basis function network (FBFN) algorithm, which includes thirteen normalized Gaussian membership functions and thirteen rules, to estimate the beam pointing error. The simulation results show that the presented FBFN processing device has better performance in transient response, convergence time and steady state value of the averaged square error than the conventional beam pointing error correction devices.

The invention relates to a time sequence remote sensing image crop classification method combining a TWDTW algorithm and a fuzzy set. The method comprises the following steps: S1, obtaining time sequence remote sensing image data, plot data and crop sample data of a to-be-detected region; S2, preprocessing the time sequence remote sensing image data; S3, constructing an NDVI time sequence data set; S4, respectively constructing standard NDVI time sequence data of different crops and an NDVI time sequence data set of the plot units; S5, constructing a TWDTW algorithm of a non-equal-length time sequence, and obtaining a minimum cumulative distance feature matched with the similarities of different crops; S6, on the basis of the NDVI time sequence data set of the plot unit, phenological characteristics of different crop growth season lengths are calculated; and S7, on the basis of the minimum cumulative distance feature and the growth season length feature, constructing Gaussian membership functions of different crops, and on the basis of a fuzzy set classification rule, realizing refined crop classification on the plot scale. According to the invention, refined classification of crops on the plot scale is realized.

The invention discloses a hybrid electric vehicle power battery initial electric quantity algorithm, comprising: ANFIS construction, ANFIS network structure determination, selection of a Gaussian membership function as an input and output membership function, division of an input variable space, and calculation of battery initial electric quantity Q1 by using a BP neural network and an ANFIS model. According to the scheme, the algorithm can monitor the electric quantity of the power battery in real time. The service life of the power battery is prevented from being influenced by overcharging or overdischarging of the power battery. Through analyzing the charging and discharging process of the battery is analyzed, key parameters of SOC is determined, the test model is corrected on the MatLab platform. Comparison by experimental simulation shows that ANFIS has good adaptive ability and generalization ability. The initial electric quantity estimation error of the battery is reduced to belower than 3%. The method can be used for an intelligent monitoring system of the hybrid electric vehicle, a temperature compensation coefficient is added, the temperature influence is corrected through the temperature compensation coefficient, and the estimation precision can be better improved.

The invention discloses a human body-behind-wall multi-state target detection method based on fuzzy pattern recognition and a genetic algorithm. The human body-behind-wall multi-state target detection method comprises processing a received signal of a P410 radar, and extracting a characteristic parameter of the received signal; and forming a membership function set by means of the extracted characteristic parameter and multiple states behind a wall. A gaussian function is selected as a sub-membership function, the mean value and variance in the sub-membership function are optimized by means of the genetic algorithm, and the membership function set is constructed; a human body-behind-wall target prediction function is established in dependence on a fuzzy pattern recognition theory, and through calculation, which kind of state the detected data belongs to can be obviously recognized on the basis of a maximum membership degree principle. The human body-behind-wall multi-state target detection method based on the fuzzy pattern recognition and the genetic algorithm is mainly applied to the disaster rescue field and the anti-terrorism criminal investigation field in order to guarantee that a target under which a living body is buried is detected and rescued and the personal safety of a hostage is guaranteed when the hostage is seized in the anti-terrorism action.

The invention discloses a driving state detecting device and method based on reverse binoculus. Determination of a current driving state of a driver is conducted according to the yaw rate of a vehicleand the postures of the head of the driver. The method comprises the steps that firstly, detection and recognition of traffic lanes are conducted according to a Hough algorithm to calculate the yaw rate of the vehicle; meanwhile estimation on the postures of the head of the driver is conducted by adopting a multi-point perspective algorithm; and then fuzzy judgement rules based on a Gause membership function are established, recognition of the driving state of the driver is conducted according to the yaw rate of the vehicle and the postures of the head of the driver, and a complete driver state detection model is established.

The invention provides an underwater multi-class target classification method based on credibility estimation. Firstly, an underwater multi-target data set is constructed, a power set of the underwater multi-target data set is given, then a classification result of each second-class SVM classifier is given, a contradiction factor and the confidence coefficient of each second-class SVM classifier are calculated, and therefore the classification accuracy of each underwater target class needing to be judged is obtained. The objective of the invention is to solve the problems that the confidence degree of each second-class SVM classifier cannot be determined and the results of a plurality of second-class SVM classifiers cannot be effectively fused. A Gaussian membership function is used to represent a reliability factor of each second-class SVM and a constructed confidence fusion rule is used to fuse an output result of each second-class SVM with the reliability factor. Therefore, multipletypes of underwater targets can be identified on the basis of increasing the credibility of each binary classifier, and the classification accuracy of the multiple types of underwater targets is improved.

The invention relates to a novel data-driven model predictive control method, which comprises the following steps of: giving correlation weight between each input variable and output from input variables obtained from a wind power plant by adopting a two-stage fuzzy curve method, and quickly selecting important input variables according to input variable indexes; then, determining prerequisite parameters of a fuzzy model by adopting fuzzy clustering and a Gaussian membership function, and identifying conclusion parameters of the fuzzy model by adopting recursive least squares; and the identified model is used for participating in data-driven model prediction control, so that the wind power plant can smoothly perform primary frequency modulation, the MPC problem for wind power plant frequency control is completed, and a relatively accurate solution can be quickly obtained.

The invention belongs to the field of image processing and provides an image description method and system based on a bag-of-words model. The method and system apply the fuzzy theory on the basis of the prior art, and use the Gaussian membership function to assign different membership degrees to each visual word after obtaining the distance set, so as to convert the distance set into a fuzzy set, and then use different The visual words of the degree of membership encode the feature points, thereby effectively reducing the information loss problem caused by traditional methods, and thus improving the accuracy of image description.

The invention relates to a cold-rolling mill second flow thickness control method and device based on fuzzy control. The method comprises the steps that the thickness of a strip steel outlet is pre-calculated according to a second flow equation, and an outlet thickness gauge is used for measuring the thickness and correcting the thickness to obtain an outlet thickness difference; the outlet thickness difference is divided into a plurality of fuzzy grades, and a subordinating degree value of the outlet thickness difference is obtained; and a fuzzy rule is determined, proportional and integral control output control amounts are calculated according to the fuzzy rule and the membership degree value, and the total output control amount is calculated according to the proportional and integral control output control amounts. According to the cold-rolling mill second flow thickness control method and device, the thickness of the strip steel outlet is firstly pre-calculated according to the second flow equation, and the outlet thickness gauge is used for measuring the thickness and correcting the thickness to obtain the outlet thickness difference; then the outlet thickness difference is divided into the multiple fuzzy grades, and a Gaussian subordinating degree function is used for obtaining the subordinating degree value; the fuzzy rule and the adaptive coefficient are set, and the control amounts of proportional control and integral control are respectively obtained; and finally the second flow AGC control amount is synthesized.

The invention discloses an on-load tap-changer state monitoring method and system based on information fusion, and belongs to the technical field of electric power, and the method comprises the steps:obtaining an effective vibration signal of an on-load tap-changer in a to-be-detected operation state; obtaining a phase point distribution track and a vectorized phase point distribution coefficientof the effective vibration signal in a phase space by adopting a phase space reconstruction technology; solving the membership degree of the to-be-measured operation state corresponding to each fuzzyset by using the vectorized phase point distribution coefficient and a multi-dimensional Gaussian membership function; and selecting an operation state corresponding to the maximum membership degreeas a to-be-tested operation state; according to the invention, the vectorized phase point distribution coefficient under each operation state is used as a characteristic information base of each operation state; the three-dimensional Gaussian fuzzy set membership function capable of simultaneously processing characteristic values of multiple channels is established, a normal state and multiple fault states can be effectively identified, and compared with an existing monitoring method based on a single channel, the information utilization rate is high, the anti-interference capability is high,and particularly, the identification rate is greatly improved.

The invention belongs to the technical field of automatic control and relates to a variable-domain fuzzy PID synchronous control method of an electro-hydraulic servo synchronous driving system. The method comprises the following steps: designing a domain expansion factor which can be adaptively adjusted according to input and output variables; selecting different input variables for a free channel and an adjustment channel and selecting [delta]KP and [delta]KI as output variables; performing fuzzy processing on the input variables; selecting a triangular membership function at the zero point of the domain, and selecting a Gaussian membership function near the boundary of the fuzzy domain; creating a fuzzy rule table; performing fuzzy reasoning with a Mamdani rule; and performing clarification on fuzzy quantity, outputting the fuzzy quantity to the controlled object for control. Compared with a traditional variable-domain fuzzy PID controller, the method has good dynamic coordination, high control precision and high versatility.

The invention provides an underwater multi-target classification method based on reliability estimation. First, an underwater multi-target data set is constructed and its power set is given, and then the classification result of each two-class SVM classifier is given, and the calculation The conflict factor and the confidence of each two-class SVM classifier are used to obtain the classification accuracy of each underwater target class that needs to be discriminated. Aiming at the problem that the confidence level of each two-class SVM classifier cannot be determined and the results of multiple two-class SVM classifiers cannot be effectively fused, the present invention uses a Gaussian membership function to represent the reliability of each two-class SVM. factor and use the constructed confidence fusion rule to fuse the output results of each two-class SVM with a confidence factor, so that the multi-class underwater targets can be identified on the basis of increasing the credibility of each binary classifier, and the water quality can be improved. Classification accuracy of multi-class targets.

The present invention proposes a high-resolution remote sensing image segmentation method based on a fuzzy Gaussian membership function, supervises sampling and extracts training samples, and calculates the frequency value at which the gray value of each pixel in the training samples appears in the corresponding feature category; Establish Gaussian membership function models for different types of ground objects; fuzzify the Gaussian membership function model parameters, and establish fuzzy membership functions; establish a linear neural network model as the objective function of high-resolution remote sensing images, and integrate the spatial relationship to obtain the target of high-resolution remote sensing images Function matrix; divide the objective function matrix of high-resolution remote sensing images according to the principle of maximum membership degree; change the adjustment factor according to the set step size, and take the optimal segmentation as the final result. Based on the boundary information of the fuzzy membership function and the original membership function, the present invention establishes the objective function and integrates the spatial relationship, realizes the accurate fitting of the complex distribution characteristics of the high-resolution remote sensing image, effectively overcomes the noise, and improves the Segmentation accuracy.

The invention discloses a network security risk assessment method based on fuzzy Bayes. The invention provides an assessment level credibility algorithm according to a European spatial vector projection thought; the algorithm synthesizes opinions of many specialists and handles a condition that many assessment results are given by the specialists for uncertainty, and then the algorithm carries out fuzzy processing on the assessment results through a Gaussian membership function, and lastly, the algorithm solves the risk faced by a tested information system through combination with an inference algorithm of a Bayes network model. According to the method, the objectivity and the effectiveness of the assessment results can be enhanced, so that a more reasonable and effective basis is provided for the subsequent risk control and management.

The invention relates to a feature recognition method for finger-palm vein fusion. A feature recognition method for finger-palm vein fusion is provided, which extracts vein pattern features of four finger areas and one palm area for matching, and fuses the matching results of five areas to identify the user's identity, which can realize reliable identity authentication. The fusion of the finger vein features of four fingers and the palm vein features of a palm area enhances the feature identification ability and improves the security of feature recognition; at the same time, the Gaussian membership function is used to carry out the matching results of the vein features of the five areas. Fusion enhances the robustness of the feature matching algorithm. This method uses the same set of vein collection equipment to collect relevant data at one time. Under the condition that the equipment cost and collection convenience are basically unchanged, the identification ability of features is enhanced, and the security performance of identity authentication is improved. It can be widely used in intelligent access control systems. .

The invention discloses a fuzzy correlation fusion method for a multi-bomb coordination system, which includes performing filtering and time-space synchronization to obtain the local estimation of each missile in the multi-bomb detection system; Fuzzy factor set; Gaussian membership function is used to calculate each fuzzy factor set to obtain the corresponding similarity value; based on the similarity value, the comprehensive fuzzy similarity of the local estimation of any two missiles is obtained through the fuzzy comprehensive function; all comprehensive The fuzzy similarity is constructed as a comprehensive fuzzy similarity matrix; the comprehensive fuzzy similarity matrix is ​​associated and judged to obtain the fusion estimation information of the multi-bomb detection system; the fusion estimation information is used to correct the detection information error of the bombs, and the fusion estimation information is used as Input from missile guidance information. The invention makes the missiles in the multi-bomb coordination system less susceptible to deception interference, and can correct the deviation in target detection.