814 results about "Fuzzy neural" patented technology

The invention relates to an eccentric pump vibration accidence fusion diagnose method and relative vibration signal collector, wherein said invention is characterized in that: it uses the eccentric pump vibration signal collector to collect the normal state, the quantity imbalance, asymmetry rotate and loose base of eccentric pump; uses wavelet decomposition and reconstruction to extract the character of vibration signal; and inputs the character vectors into sub fuzzy neural networks I and II; to be treated and replace the relation factor matched with sensor signal function; the whole fuzzy neural network comprises data fuzzy layer, input layer, hidden layer and output layer; uses D-S theory to obtain the fused signal function distribution, realize the fusion diagnose on normal state, quantity imbalance, asymmetry rotate and loose base. The invention has simple structure and high effect.

The invention discloses a variable-impedance lower limb rehabilitation robot control method based on brain muscle information. The method includes: collecting electroencephalogram and surface electromyogram signals of a patient in real time through an electroencephalogram and surface electromyogram signal collector, and monitoring and evaluating rehabilitation degree of the patient; adopting different rehabilitation training strategies; when the rehabilitation degree is low, implementing passive training control, adopting a PD position servo control method, and controlling a lower limb rehabilitation device to enable the patient to move with a correct physiological gait track; when the rehabilitation degree is high, adopting an active control mode, and predicting a movement intention of the patient by extracting feature vectors of electroencephalogram signals and surface electromyogram signals of the patient in real time; using a fuzzy neutral network algorithm to integrate the electroencephalogram signals and the surface electromyogram signals to generate a movement gait track curve expected by the patient in real time; utilizing a variable-impedance control method to realize active, realtime and synergistic control of a lower limb rehabilitation robot man-machine system.

The invention provides an adaptive management method for the power consumption of a data center. The method comprises the following steps of: partitioning a computer room according to functions of each part, arranging N+1 precision air conditioners in each function area, and sorting by priority according to the importance of a refrigerating action; adjusting the power consumption of a server in real time by using monitoring software according to a service load in the computer room; accomplishing generation of an air conditioner adjustment strategy with the help of a computational fluid dynamics (CFD) simulation computed result; and controlling the air conditioners of the computer room by using a fuzzy neural network control strategy. By the method, an optimum value can be achieved under the conditions of good radiation effect and low energy consumption.

The invention relates to a method and a system for detecting a road barrier. The invention discloses a first barrier detection model based on a video pick-up device, a second barrier detection model based on the video pick-up device and a millimeter-wave radar, and a third barrier detection model based on a three-dimensional laser radar and an infrared pick-up device, wherein complementary detection for the multiple models is formed through a rough set based fuzzy neural network algorithm, so that characteristic information of the road barrier can be obtained in real time. The method and system for detecting the road barrier can carry out real-time and effective road barrier detection under the conditions of different road scenes and different weathers to obtain parameters such as the travelling speed and direction of different barriers exactly, can extract and analyze surrounding environment information of vehicles from the road traffic environment, and can judge abnormal traffic behaviors so as to relieve the current urban traffic pressure and improve traffic management efficiency.

The invention discloses a fatigue driving fusion detection method based on soft computing, which can detect the fatigue driving of the driver and is characterized in that: the fatigue driving is detected in fusion mode through two aspects which include two facial characteristics for directly indicating the fatigue state of the driver and two vehicle behavior characteristics for indirectly indicating the fatigue state of the driver, wherein the two facial characteristics respectively are frequent blinking and yawning, and the two vehicle behavior characteristics respectively are abnormal vehicle lane deviation and abnormal steering wheel rotation; the invention utilizes the TS fuzzy neural network to recognize fatigue driving, adopts abstraction clustering for the optimized recognition of the network structure, and determines the number of fuzzy rules of the fuzzy neural network and the initial values of the relevant network parameters; genetic algorithm is utilized to train and optimize the network parameters, and determine the optimum network parameters; the TS fuzzy neural network is utilized to detect the fatigue driving of the driver in real-time according to the optimum network parameters and the four fatigue characteristic parameters.

The system comprises preprocessing board connecting key phase signal to fast signal, key phase board controlling whole period of sampling, high speed collecting board for vibration signal, low speed data collecting board for graded signal. In the method, they are connected to down level server to fulfil data collection, signal analysis, and automatic identification as well as picking up characteristic parameters of operatino of machine set and down level server can make high speed communication with up level server through network card or IP protocol as up level server is equipped with an intelligent reasoning machine integrating professional knowledge rule, fuzzy logic and neural network in one component to carry trend prediction and intelligent diagnosis for operation and failure of machine.

The invention provides a wind-field power combination predicting method based on a fuzzy neural network and a support vector machine, which comprises the following steps of: acquiring and pre-processing data; setting up a fuzzy neural network model by using a normalized training sample set and a prediction sample set and predicting; setting up a support vector machine model and predicting; linearly combining the prediction results of the two algorithms to obtain a wind speed prediction value; and setting up a wind speed power expert table via historical data, and inquiring the expert table according to the predicted wind speed value so as to obtain a power prediction result. By the method provided by the invention, the short-term prediction of a wind speed sequence can be effectively realized, the power prediction precision is improved, and fewer computing resources are consumed.

A method and system for wastewater treatment based on dissolved oxygen control by a fuzzy neural network, the method for wastewater treatment comprising the following steps: (1) measuring art inlet water flow rate, an ORP value in an anaerobic tank, a DO value in an aerobic tank, an inlet water COD value, and an actual outlet water COD value; (2) collecting the measured sample data and sending them via a computer to a COD fuzzy neural network predictive model, so as to establish an outlet water COD predicted value, (3) comparing the outlet COD predicted value with the outlet water COD set value, so as to obtain an error and an error change rate, and using them as two input variables to adjust a suitable dissolved oxygen concentration. Accordingly, the on-line prediction and real-time control of dissolved oxygen wastewater treatment are achieved. The accurate control of dissolved oxygen concentration by the present method for wastewater treatment can achieve a saving in energy consumption while ensuring stable running of the sewage treatment system, and the outlet water quality meets the national emission standards.

The invention pertains to the field of the image fusion technology in image process, which relates to a quality comprehensive evaluation method of fusion images based on fuzzy neural network and comprises the following steps: a sample set of fusion images is established, and each group of samples comprises a subjective evaluation grade sample of fusion images and two or more than two objective evaluating indicator samples obtained by evaluating the fusion image objectively; a quality evaluation module of fusion images based on fuzzy neural network is established; the obtained samples are trained, and the subjective evaluation grade sample of fusion images is adopted as expected output, and the correlation parameters for evaluating indicator weighing and fuzzy membership function are generated through network learning; the objective evaluating indicator of fusion images to be evaluated is calculated, and the evaluation grade result is generated by taking advantage of the established fusion image quality evaluation module. The method of the invention has comparatively good flexibility, and in the way of network training, novel fusion image quality evaluating indicator is learnt, so as to expand network evaluation ability and realize completely automatic evaluation.

The invention discloses a method for battery SOC estimation based on small model error criterion expanding Kalman filter. The method comprises the steps of first establishing a variable-order RC model based on an AIC criterion and laying a good foundation for SOC precise estimation; obtaining data such as battery terminal voltage, current and corresponding model errors in an off-line mode under different working conditions and establishing a model error prediction model based on a fuzzy neural network; predicting model errors in an on-line mode based on the neural network in filtering, and performing measurement and updating on state estimation only when predicted errors are small so that the problem of filtering divergence caused by the model errors and system noise statistical characteristic uncertainty and the problem of SOC estimation fluctuation caused by battery terminal voltage jump can be solved. The method can effectively eliminate filtering estimation errors caused by the model errors and can be suitable for the dynamic process of a battery under various complex working conditions.

The invention discloses a short-term traffic flow weighted combination prediction method, which comprises the following steps of: (1) organizing historical traffic flow data by utilizing a dynamic clustering algorithm; (2) performing short-term traffic flow prediction by using an improved nearest neighbor nonparametric regression method; (3) performing the short-term traffic flow prediction by taking a cluster which is the most similar to a current point in a historical database as a training sample of a fuzzy neural network and using a fuzzy neural network model; and (4) determining the weight of a combined prediction method according to a prediction error of the improved nearest neighbor nonparametric regression method and the fuzzy neural network model in the last time bucket, and outputting a final prediction result in a weighted combination way. A traffic flow in the last time bucket and a traffic flow of related turning at an upstream road junction are taken into account, the training sample of the fuzzy neural network is optimized, and the final prediction result is output in the weighted combination way, so that short-term traffic flow prediction accuracy and real-time performance are improved.

The invention provides a multi-neural network control planning method for a robot path in an intelligent environment. The method comprises the steps that 1 a global map three-dimensional coordinate system is constructed for the carrying area of a carrier robot to acquire a walkable area coordinate in the global map three-dimensional coordinate system; 2 a training sample set is acquired; 3 the global static path planning model of the carrier robot is constructed; and 4 starting and ending coordinates in a transportation task are input into the global static path planning model based on a fuzzy neural network to acquire the corresponding optimal planning path for the carrier robot. According to the invention, the global static path planning model and a local dynamic obstacle avoidance planning model are separately established; the nonlinear fitting property of the neural network is used to find the global optimal solution quickly; and the problem of falling into a local optimum in common path planning is avoided.

The invention provides a multiple-target operation optimizing and coordinating control method and a device of a garbage power generator. The multiple-target operation optimizing and coordinating control method includes the following steps. Operational parameters are downloaded from a data communication system (DCS), data judged as reasonable based on a threshold value are transmitted to a database. In terms of environmental protection, economy and safety of the power generator, three models are respectively set up by means of a support vector machine and a fuzzy neural network. A modified strength PARETO genetic algorithm is used for comprehensively optimizing multiple targets and then optimum operation parameters under the present working condition are worked out. Operational staff can adjust operation of corresponding parts based on the optimum operation parameters. The device comprises a data collecting module, a data filtering module, a database module, a data modeling module, an optimizing module, a forecasting module, a remote monitoring module, a monitor, an alarming module and a manual alarming module. The multiple-target operation optimizing and coordinating control method and the device of the garbage power generator achieve multiple functions of real-time forecasting, offline simulation, dynamic optimizing and the like and have the advantages of being strong in adaptability, good in self-learning ability, high in fitting precision, obvious in optimizing effect and the like.

The invention relates to a motor rotating-speed tracking control method based on a self-adaptive fuzzy neural network, wherein rotating-speed and current double closed-loop control is adopted, an outer ring is a rotating-speed ring, a sliding-mode control theory-based fuzzy neural-network controller (SMFNN) is designed, an inner ring is a current ring, and a PI (Proportional-Integral) controller is adopted; a fuzzy neural-network rotating-speed controller comprises two parts, wherein one part is a PID (Proportional-Integral-Derivative) controller, and the other part is the fuzzy neural network, online real-time learning is carried out through the fuzzy neural network by utilizing a parameter correcting method designed on the basis of the sliding-mode control theory, and the two parts jointly act to obtain the output ir of the rotating-speed controller, i.e. a difference obtained by subtracting the output iFNN of the fuzzy neural network from the output iPID of the PID controller is used as the output ir of the rotating-speed controller. The control precision and the anti-interference performance of a motor speed-adjusting system can be improved through the control strategy of the motor rotating-speed tracking control method.

The invention relates to a highway tunnel illumination energy-saving intelligent control system. In the system, according to a fuzzy neural network control theory, a relation model of the brightness outside a highway tunnel and the brightness of LED (Light-Emitting Diode) tunnel lamps at an inlet and a transition section of a tunnel and a relation model of the brightness of the LED tunnel lamps and the tunnel vehicle flow are established and the intelligent regulation and control of the brightness of the LED tunnel lamps are implemented; the system consists of an outside tunnel brightness detector (1), an in-tunnel brightness detector (3), a vehicle detector (2), signal processing modules (4), an illumination control computer (5), driving power supplies (6) and the LED tunnel lamps (7); and information of the vehicle flow, the vehicle speeds, the outside tunnel brightness and in-tunnel brightness which are acquired by the vehicle detector and the in-tunnel brightness detector and the outside tunnel brightness detector are transmitted to the illumination control computer (5) after being processed by the signal processing modules (4) and the output powers of the LED tunnel lamps (7) are regulated according to a command of the illumination control computer, so that the continuous dimming in the tunnel is implemented. The highway tunnel illumination energy-saving intelligent control system is suitable for illumination energy-saving control of the highway tunnel.

The invention provides a method for predicting the service life of a screw pair of a numerical control machine on the basis of a performance degradation model. The method comprises the following steps of: acquiring vibration signals, carrying out time-frequency domain analysis, extracting the sensitive characteristic data vectors of the performance degradation of the screw pair, and forming a sensitive characteristic matrix in a time-sequence manner; calculating the load Pi of the screw pair and recording the operating time ti at the same time; calculating the rated life time Lhi, the total time t' that the screw pair has run under the current working condition, and the expected residual life LDi according to Pi, and forming an expected residual life vector T of the expected residual life in a time-sequence manner; and fitting the mapping relation between the inputted sensitive characteristic matrix and the expected residual life vector by using a degradation model formed by a double-layer dynamic fuzzy neural network, and outputting the prediction result of the service life. By taking the impact on the performance degradation of the screw pair caused by the load change thereof under various working conditions of the numerical control machine into consideration, the method of the invention can achieve the prediction of the residual life when the screw pair is used, and ensure the high prediction accuracy and high value in actual application.

The invention discloses a fault tree and fuzzy neural network based automobile crane fault diagnosis method. The method includes: (1) establishing a top event fault tree of an automobile crane by a deductive method; (2) determining the numbers of input and output nodes of a fuzzy neural network according to fault tree branches and experiential knowledge, and establishing a structural model of the fuzzy neural network; (3) extracting a training sample according to knowledge contained in each branch of the fault tree, training the neural network, and establishing a network weight and a threshold matrix needed for neural network reasoning and calculation; (4) monitoring data on a platform by the aid of an existing automobile crane state, and applying a 3sigma criteria method in a statistical parameter method for determining a fuzzy membership function needed for fuzzy preprocessing; (5) inputting measured data into the fuzzy neural network for calculation, and outputting a fault mode. By the method, blindness and complexity during detection are avoided, and accuracy rate of diagnosis is increased.

The invention provides a power utilization abnormal behavior recognition method based on a fuzzy neural network, and the method comprises the steps: extracting original data of a part of users as sample data from a power utilization database; carrying out the data preprocessing; designing a power utilization abnormal behavior evaluation index system on the basis of the analysis of power utilization abnormal behavior cases; constructing an expert sample based on the preprocessed data; taking an power utilization abnormal behavior mark as an input item, taking a power utilization abnormal behavior suspicion coefficient as an output item, and constructing a fuzzy neural network model; inputting test data into a built fuzzy neural network model, and carrying out the diagnosis of the power utilization abnormal behavior; evaluating a power utilization abnormal behavior diagnosis result, and a setting a target evaluation and optimization model. The method provided by the invention achieves the automatic recognition and diagnosis of the power utilization abnormal behavior, achieves the automatic training learning and modeling of a system, achieves the quick and precise positioning of a suspected user, and facilitates the obtaining of various illegal behaviors of power utilization.

The invention provides an algorithm for implementing global navigation satellite system (GNSS) and WIFI system seamless vertical handoff based on a fuzzy neural network algorithm in order to realize a purpose of fusing two different positioning systems mutually to implement seamless handoff of indoor and outdoor positioning and navigation. The algorithm comprises the following steps: acquisition of main positioning information, handoff triggering based on received signals, and handoff algorithm judgment based on fuzzy neural network control. The algorithm for implementing the GNSS and WIFI system seamless vertical handoff has the beneficial effect of overcoming a defect that WIFI position precision is reduced outdoor and GNSS signals have a possible of occurring interruption indoor so as to provide wrong positioning information for a user; America GPS, Russia Glonass, EU Galileo and China Beidou satellite have a large development on satellite positioning and navigation application technology positioning aspects indoor, outdoor, underground, on the earth surface and the like; and meanwhile, the algorithm provided by the invention has wide applications in the fields of national military applications, international counter terrorism, tunnel detection and the like.

The invention discloses a combustion optimization control method for a boiler. The combustion optimization control method is characterized by comprising the following steps: sampling a combustion nonlinear system of the boiler to obtain input / output data at the current moment; training the real-time sampled input / output data by an online incremental learning fuzzy neural network, building an online incremental learning predicting model of the combustion nonlinear system of the boiler; performing a nonlinear prediction control algorithm on the online incremental learning predicting model for realizing the optimization and the control of the combustion process of the boiler. According to the combustion optimization control method for the power station boiler of the online incremental learning fuzzy neural network, the nonlinear optimization problem in the predication control algorithm is solved by utilizing a particle swarm optimization algorithm through the online identification of the boiler combustion optimization model; the real-time optimization and control of the boiler combustion process are realized.

The invention discloses network flow-predicting method and device based on wavelet package decomposition and a fuzzy neural network, wherein the method comprises the steps of: collecting historic measured data of network flow; decomposing the original network flow onto wavelets with different time scales by wavelet package conversion; reconstructing flow signals on all time scales so as to lead the data volume of the flow signals to be identical with that of original signals; by the fuzzy neural network, studying and predicting the flow signals decomposed onto all time scales; and summing thepredicted values of the flow signals on all time scales to obtain the predicted value of network flow.

The invention discloses a robust controller of a permanent magnet synchronous motor based on a fuzzy-neural network generalized inverse and a construction method thereof. The construction method of the invention comprises the following steps of: combining an internal model controller and a fuzzy-neural network generalized inverse to form a compound controlled object; serially connecting two linear transfer functions and one integrator with the fuzzy-neural network with determined parameters and weight coefficients to form the fuzzy-neural network generalized inverse, serially connecting the fuzzy-neural network generalized inverse and the compound controlled object to form a generalized pseudo-linear system, linearizing a PMSM (permanent magnet synchronous motor), and decoupling and equalizing the linearized PMSM into a second-order speed pseudo-linear subsystem and a first-order current pseudo-linear subsystem; and respectively introducing an internal-model control method in the two pseudo-linear subsystems to construct the internal model controller. The robust controller of the invention has the advantages of overcoming the dependence and local convergence of the optimal gradient method on initial values and solving the problems of randomness and probability caused by using the simple genetic algorithm, obtaining the high performance control, anti-disturbance performance and adaptability of the motor and simplifying the control difficulty, along with simple structure and high system robustness.

The invention discloses a driver fatigue and emotion evaluation method based on multi-source physiological information. The method comprises the following steps of 1, simultaneously collecting a driver's EEG, ECG, EMG and attitude signals; 2, performing pretreatment and feature extraction on the physiological signals; 3, building a fuzzy neural network evaluation model to achieve driver fatigue and emotion evaluation; and 4, based on the evaluation model, using genetic algorithms for continuously learning the driver's evaluation index, extracting rules and methods of driver fatigue and emotionevaluation, and improving the evaluation accuracy. The method emphasizes the comprehensiveness of decision information and the advanced nature of a classification method, greatly improves the accuracy of driver fatigue and emotion evaluation, and reduces the probability of occurrence of traffic accidents.

A Soft Computing (SC) optimizer for designing a Knowledge Base (KB) to be used in a control system for controlling a suspension system is described. The SC optimizer includes a fuzzy inference engine based on a Fuzzy Neural Network (FNN). The SC Optimizer provides Fuzzy Inference System (FIS) structure selection, FIS structure optimization method selection, and teaching signal selection and generation. The user selects a fuzzy model, including one or more of: the number of input and / or output variables; the type of fuzzy inference model (e.g., Mamdani, Sugeno, Tsukamoto, etc.); and the preliminary type of membership functions. A Genetic Algorithm (GA) is used to optimize linguistic variable parameters and the input-output training patterns. A GA is also used to optimize the rule base, using the fuzzy model, optimal linguistic variable parameters, and a teaching signal. The GA produces a near-optimal FNN. The near-optimal FNN can be improved using classical derivative-based optimization procedures. The FIS structure found by the GA is optimized with a fitness function based on a response of the actual suspension system model of the controlled suspension system. The SC optimizer produces a robust KB that is typically smaller that the KB produced by prior art methods.

The invention discloses a fuzzy nerve network control method for an automobile driving robot system. The method comprises: performing speed tracking on a test vehicle from the characteristic parameters of an automobile driving robot gearshift manipulator, a throttle mechanical leg, a brake mechanical leg and a clutch mechanical leg; utilizing a Sugeno fuzzy nerve network to realize the speed tracking and control of an automobile driving robot, inputting characteristic parameters, adopting a generalized bell-shaped function for a membership function in terms of type, using a mixing learning algorithm to train and optimize network parameters, and determining optimum network parameters; and according to the optimum network parameters and four characteristic parameters detected in real time of the automobile driving robot, using the Sugeno fuzzy nerve network to perform speed tracking and accurate control on the automobile driving robot. The method disclosed by the invention is provided with an on-line self-learning capability, can accurately track a given target speed, and has good robustness in various test conditions.

The invention discloses a method for monitoring and controlling environments of a public place based on Zigbee, which comprises the following steps of: collecting environmental parameters in a public place by using a wireless sensor network, sending the acquired environmental parameters to a controller, and carrying out fuzzy modeling and chaotic modeling on the acquired environmental parameters by the controller; establishing a fuzzy neural network according to an established environmental parameter model, and carrying out real-time control on a working apparatus by utilizing the fuzzy neural network; and carrying long-term control on the working apparatus in the public place by utilizing a chaotic perturbation control method. In the invention, the fuzzy neural network and the chaotic perturbation control method are combined together, and the working apparatus is controlled according to season change of natural conditions, such as sunshine, temperature, and the like at the site of the public place so as to achieve the purposes of energy saving and humanization. Furthermore, the wireless sensor network adopts a wireless sensor network based on Zigbee, has the characteristics of an ad hoc network and low power consumption and avoids the arrangement difficulty of a collection point when the traditional cable is wired.

The method comprises two stages -a training stage and a test stage. The training stage comprises: getting the connection vector of the input network from the network connection data vector training sample set; making a feature selection and a feature conversion for it to generate a feature vector; sending the feature vector to the fuzzy neural network; using ANFIS to make training, and until it is stabilized to get the fuzzy neutral network model. The test stage comprises: in the first, getting the network connection vector from the network connection data vector training sampling set; after making pre-process, generating a feature vector; inputting the feature vector into the trained fuzzy neural network to get relevant output value; finally, making the fuzzy clustering for the output value set.

Methods and systems for controlling a prosthesis using a brain imager that images a localized portion of the brain are provided according to one embodiment of the invention. For example, the brain imager can provide motor cortex activation data using near infrared imaging techniques and EEG techniques among others. EEG and near infrared signals can be correlated with brain activity related to limbic control and may be provided to a neural network, for example, a fuzzy neural network that maps brain activity data to limbic control data. The limbic control data may then be used to control a prosthetic limb. Other embodiments of the invention include fiber optics that provide light to and receive light from the surface of the scalp through hair.