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33 results about "Nonlinear system identification" patented technology
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System identification is a method of identifying or measuring the mathematical model of a system from measurements of the system inputs and outputs. The applications of system identification include any system where the inputs and outputs can be measured and include industrial processes, control systems, economic data, biology and the life sciences, medicine, social systems and many more.
Nonlinear System Identification Techniques and Devices for Discovering Dynamic and Static Tissue Properties
ActiveUS20110054354A1Quickly mechanical propertyLow costDiagnostics using suctionDiagnostics using pressureAccelerometerEngineering
A device for measuring a mechanical property of a tissue includes a probe configured to perturb the tissue with movement relative to a surface of the tissue, an actuator coupled to the probe to move the probe, a detector configured to measure a response of the tissue to the perturbation, and a controller coupled to the actuator and the detector. The controller drives the actuator using a stochastic sequence and determines the mechanical property of the tissue using the measured response received from the detector. The probe can be coupled to the tissue surface. The device can include a reference surface configured to contact the tissue surface. The probe may include a set of interchangeable heads, the set including a head for lateral movement of the probe and a head for perpendicular movement of the probe. The perturbation can include extension of the tissue with the probe or sliding the probe across the tissue surface and may also include indentation of the tissue with the probe. In some embodiments, the actuator includes a Lorentz force linear actuator. The mechanical property may be determined using non-linear stochastic system identification. The mechanical property may be indicative of, for example, tissue compliance and tissue elasticity. The device can further include a handle for manual application of the probe to the surface of the tissue and may include an accelerometer detecting an orientation of the probe. The device can be used to test skin tissue of an animal, plant tissue, such as fruit and vegetables, or any other biological tissue.
Owner:MASSACHUSETTS INST OF TECH
Aircraft flutter prediction and analysis method and device
ActiveCN104182560AImprove accuracySpecial data processing applicationsRecognition algorithmSimulation
The invention discloses an aircraft flutter prediction and analysis method and device. The method comprises the following steps: applying an input signal through an exciting point on an aircraft structure, and receiving an output signal through a collection point which is configured on the aircraft structure; establishing a nonlinear kinetic model of the aircraft structure by utilizing a nonlinear system recognition algorithm according to the input signal and the output signal; establishing an unsteady aerodynamic model on the basis of the calculation fluid kinetic technology; performing reduced order for the unsteady aerodynamic model according to the nonlinear kinetic model of the aircraft structure to obtain a reduced order model; performing the fluid-structure interaction analysis on the nonlinear kinetic model and the reduced order model so as to make the flutter prediction and analysis on the corresponding aircraft structure. Since the nonlinear kinetic model of the aircraft structure is acquired by performing the test according to the real aircraft structure, the accuracy in flutter prediction and analysis can be improved.
Owner:BEIJING AERONAUTIC SCI & TECH RES INST OF COMAC +1
Loudspeaker nonlinear system identification method
ActiveCN106068007AImprove accuracyIncrease flexibilitySubsonic/sonic/ultrasonic wave measurementUsing electrical meansLinear componentSignal on
The invention provides a loudspeaker nonlinear system identification method comprising the steps of providing an amplified excitation signal for a loudspeaker system to be tested; synchronizing a voltage signal and current signal on both ends of the loudspeaker system; acquiring a linear parameter of the loudspeaker system: according to the measured voltage signal and the measured current signal, computing the impedance curve of the loudspeaker system on a large signal condition, and matching the impedance curve by using a least square method so as to obtain the linear parameter of the loudspeaker system; acquiring the nonlinear parameter of the loudspeaker system: inputting the measured current signal into the lumped parameter model of the loudspeaker system to compute an estimated voltage signal; comparing the estimated voltage signal with the measured voltage signal and computing a voltage error signal between the estimated voltage signal with the measured voltage signal; de-correlating the voltage error signal in order remove the linear component in the voltage error signal, and acquiring the nonlinear parameter by an adaptive iteration algorithm according to the de-correlated voltage error signal.
Owner:AAC TECH PTE LTD
Nonlinear system identification techniques and devices for discovering dynamic and static tissue properties
ActiveUS8758271B2Low costProcedure can be fast and accurateDiagnostics using suctionDiagnostics using pressureAccelerometerMechanical property
A device for measuring a mechanical property of a tissue includes a probe configured to perturb the tissue with movement relative to a surface of the tissue, an actuator coupled to the probe to move the probe, a detector configured to measure a response of the tissue to the perturbation, and a controller coupled to the actuator and the detector. The controller drives the actuator using a stochastic sequence and determines the mechanical property of the tissue using the measured response received from the detector. The probe can be coupled to the tissue surface. The device can include a reference surface configured to contact the tissue surface. The probe may include a set of interchangeable heads, the set including a head for lateral movement of the probe and a head for perpendicular movement of the probe. The perturbation can include extension of the tissue with the probe or sliding the probe across the tissue surface and may also include indentation of the tissue with the probe. In some embodiments, the actuator includes a Lorentz force linear actuator. The mechanical property may be determined using non-linear stochastic system identification. The mechanical property may be indicative of, for example, tissue compliance and tissue elasticity. The device can further include a handle for manual application of the probe to the surface of the tissue and may include an accelerometer detecting an orientation of the probe. The device can be used to test skin tissue of an animal, plant tissue, such as fruit and vegetables, or any other biological tissue.
Owner:MASSACHUSETTS INST OF TECH
Nonlinear system recognizing method based on multi-target genetic programming
InactiveCN104616062AWide applicabilityReduce computational complexityGenetic modelsAlgorithmNonlinear system identification
The invention discloses a nonlinear system recognizing method based on multi-target genetic programming. The method mainly overcomes the shortcomings of low recognizing accuracy and low solving speed of the traditional recognizing method under the condition that the structure of a nonlinear system is unknown. A plurality of target optimization function models optimizing the structure of the nonlinear system and the system parameters are provided. A novel multi-target genetic programming method is adopted to solve the model. A novel evaluation method aiming at multiple optimization targets is provided, a decision making process is blended in the optimization process, calculation of multiple Pareto optimum solutions is not required, and the evaluation process is high in efficiency. The method can effectively excavate the nonlinear relation between a large amount of input-output data, and improve recognizing efficiency and accuracy.
Owner:HOHAI UNIV
Nonlinear system identification for object detection in a wireless power transfer system
ActiveCN104969438AElectromagnetic wave systemCurrent/voltage measurementReceiver coilDynamic system model
A method of detecting whether a receiver coil is near a transmit coil in a wireless power transfer system (WPTS), the method involving: applying a pseudo-random signal to the transmit coil; while the pseudo-random signal is being applied to the transmit coil, recording one or more signals produced within the WPTS in response to the applied pseudo-random signal; by using the one or more recorded signals, generating a dynamic system model for some aspect of the WPTS; and using the generated dynamic system model in combination with stored training data to determine whether an object having characteristics distinguishing the object as a receiver coil is near the transmit coil.
Owner:NUCLEUS SCI
Improved generalized prediction control method for work arm of underwater robot
InactiveCN107942671AFast convergenceImprove recognition accuracyAdaptive controlComponent Object ModelOptimal control
The invention discloses an improved generalized prediction control method for a work arm of an underwater robot. A T-S fuzzy identification model suitable for nonlinear system identification is established, a real-time CARIMA parameter model of the underwater arm is obtained by identification, a step factor is introduced to form a progressive relation, a progressive generalized prediction controlalgorithm is used to generate an optimal control variable via rolling prediction optimization, and a controller of a hydraulic driver is acted on, so that the underwater arm generates a correspondingmotion. According to the improved algorithm of the invention, it is not required to know the structure of a controlled object model, dynamic change of the system can be reflected timely, tedious matrix calculation processes are omitted, the computational complexity is reduced, and the algorithm is simpler and more practical.
Owner:STATE GRID INTELLIGENCE TECH CO LTD
Non-linear system identification method based on particle swarm
InactiveCN102055694AReduce fitnessReduce computational complexityTransmitter/receiver shaping networksSynchronous/start-stop systemsDesign objectiveComputer science
The invention discloses a non-linear system identification method based on a particle swarm, which mainly aims at the disadvantages of descending of the astringency property caused by high sensibility of additive noise by the traditional identification method. The implementation steps are: setting the highest order, a maximal memory length and a coefficient vector of a non-linear system; determining a constraint condition of an identification problem and designing an object function; setting a parameter of the particle swarm, and generating an initial speed matrix and a spatial position matrix of the particle swarm; calculating the optimum solution and the optimum fitness of the particle swarm according to the spatial position matrix and the object function of the particle swarm; updatingthe particle swam speed and spatial position matrix according to particle swarm speed updating formula and the spatial position; and finishing identification if the particle swarm optimum fitness or times of iteration satisfies the astringency condition. By the invention, the sensibility of the additive noise can be reduced, and the identification performance of the non-linear system under the additive noise condition can be improved.
Owner:XIDIAN UNIV
Dynamic and static data hybrid driven Hammerstein nonlinear industrial system simple grey box space identification method
The invention relates to a dynamic and static data hybrid driven Hammerstein nonlinear industrial system simple grey box space identification method, and belongs to the field of control theory and control engineering nonlinear system identification. The method comprises the following steps: S1, collecting dynamic data and static data of a system; S2, selecting and processing a Hammerstein nonlinear system model to obtain a prediction model; S3, constructing a dynamic simple model of the Hammerstein nonlinear system; S4, constructing a static simple model of the Hammerstein nonlinear system; and S5, solving system parameter fusion identification by using a hierarchical Lagrange optimal weighting method. Dynamic and static data are adopted at the same time, and based on the decomposed simplemodel, the estimation of additional intermediate parameters is avoided, the variance of the estimation model is reduced, and the model precision is improved.
Owner:CHONGQING UNIV OF POSTS & TELECOMM
Nonlinear system identification method
ActiveCN111325308AInternal combustion piston enginesCharacter and pattern recognitionEngineeringError function
The invention relates to a nonlinear system identification method which comprises the following steps: S1, selecting a plurality of different nonlinear systems, collecting a plurality of discrete samples corresponding to each nonlinear system, and forming a training set by using all the discrete samples of all the nonlinear systems; s2, performing dimension raising processing on discrete samples in the training set; s3, constructing a width learning model, and performing optimization training on model parameters of the width learning model through the training set subjected to dimension raising processing and a particle swarm optimization algorithm to enable the value of an error function corresponding to the trained width learning model to be minimum; and S4, identifying the nonlinear system through the trained width learning model. According to the method, the nonlinear system is identified through a method of combining width learning and a particle swarm optimization algorithm, andthe method has obvious advantages in the aspects of prediction precision and interference resistance.
Owner:JIMEI UNIV
Converter arrangement and method for converting an analogue input signal into a digital output signal
ActiveUS20150180495A1Simplifies and speeds up identification processImprove system robustnessElectric signal transmission systemsAnalogue-digital convertersNonlinear distortionNonlinear model
Owner:KLIPPEL WOLFGANG
Method for identifying system containing saturation nonlinearity
InactiveCN106950830AThe identification is correct and effectiveGet saturation limitAdaptive controlSystems designState variable
The invention provides a method for identifying a nonlinear system with saturation, and mainly provides a solution to the problem that conventional system identification methods cannot be directly used for identification of a nonlinear system with saturation. The invention designs the input signal from two aspects of frequency and amplitude to fully excite the system. A method of adding window and scanning to process each state variable of the system is proposed. By screening the variance and average value of each window data, the saturation limit of the system can be identified. According to the obtained saturation limit, the input and output data of the system are screened, and the system model parameters are obtained by using the relevant system identification method. The invention has the advantages of designing the input signal to fully excite the system for a saturated nonlinear system, correctly and effectively identifying the system model parameters, and simultaneously obtaining the system saturation limit value, which has better engineering practicability.
Owner:TSINGHUA UNIV
Identification method for nonlinear system with disturbance
ActiveCN111427266AImprove tracking performanceEasy to trackTotal factory controlAdaptive controlControl engineeringComputer science
The invention discloses an identification method for a nonlinear system with disturbance, and the method comprises the following steps: A, converting a to-be-identified industrial control system intoa nonlinear system model with disturbance, wherein the nonlinear system consist of a nonlinear link and a linear link, i.e., a Hammerstein system for outputting an error types; B, decomposing the nonlinear system model with disturbance into two sub-models: a system noise-free output sub-model and a disturbance sub-model; and C, updating system parameters to construct P [zeta] (k) and e [zeta] (k),updating the parameters to enable k to be equal to k + 1, returning to the step A until a cut-off condition D is met, and identifying the parameters and disturbance of the industrial control system.According to the invention, defects in the prior art can be overcome, the convergence speed is high, and the identification precision is high.
Owner:NORTH CHINA INST OF AEROSPACE ENG
Nonlinear system identification method based on structure adaptive filtering
The invention provides a nonlinear system identification method based on structure adaptive filtering, which comprises the following steps: obtaining input data and corresponding output data of an identification object as a data set, and dividing the data set into a training set and a test set; constructing a self-adaptive filtering model: randomly selecting a first-order subsystem and a second-order subsystem according to the generation parameters, and randomly cascading the subsystems according to a connection rule to form a complete self-adaptive filtering model; initializing an evolutionary algorithm, and selecting an optimal adaptive filtering model; carrying out loop iteration through an evolutionary algorithm, and finding the optimal structure and parameters of the optimal adaptivefiltering model; and testing by using the trained adaptive filtering model to obtain the actual output of the identification object. According to the method, the nonlinear process of the typical liquid saturated steam heat exchanger is learned by utilizing the nonlinear adaptive filtering model, and the optimal estimation can be found; the method is simple in operation, small in error, high in precision and few in used parameters, and has remarkable nonlinear system recognition capability.
Owner:HENAN UNIVERSITY
Aircraft Flutter Prediction Analysis Method and Device
ActiveCN104182560BImprove accuracySpecial data processing applicationsRecognition algorithmSimulation
The invention discloses an aircraft flutter prediction and analysis method and device. The method comprises the following steps: applying an input signal through an exciting point on an aircraft structure, and receiving an output signal through a collection point which is configured on the aircraft structure; establishing a nonlinear kinetic model of the aircraft structure by utilizing a nonlinear system recognition algorithm according to the input signal and the output signal; establishing an unsteady aerodynamic model on the basis of the calculation fluid kinetic technology; performing reduced order for the unsteady aerodynamic model according to the nonlinear kinetic model of the aircraft structure to obtain a reduced order model; performing the fluid-structure interaction analysis on the nonlinear kinetic model and the reduced order model so as to make the flutter prediction and analysis on the corresponding aircraft structure. Since the nonlinear kinetic model of the aircraft structure is acquired by performing the test according to the real aircraft structure, the accuracy in flutter prediction and analysis can be improved.
Owner:BEIJING AERONAUTIC SCI & TECH RES INST OF COMAC +1
Feedforward neural network structure self-organization method based on neuron significance
ActiveCN107273971AImprove rationalityImprove scienceNeural architecturesNeural learning methodsHidden layerNetwork structure
The invention discloses a feedforward neural network structure self-organization method based on neuron significance, which relates to the technical field of neural networks. In view of the problem that the traditional feedforward neural network structure is hard to select, the method disclosed by the invention dynamically adjusts the neural network structure according to the significance size of neurons in a hidden layer. An experiment result shows that the improved algorithm can reduce blindness of network structure selection, dynamic optimization and adjustemnt on the network structure are realized, and the network recognition precision is improved. Highp recision is realized in nonlinear system identification, data classification and engineering defect class recognition.
Owner:SHIJIAZHUANG TIEDAO UNIV
Wiener nonlinear system identification method based on parameter separation
ActiveCN111399477AReduce computationHigh precisionProgramme controlElectric testing/monitoringAlgorithmEngineering
Owner:NORTH CHINA INST OF AEROSPACE ENG
Converter arrangement and method for converting an analogue input signal into a digital output signal
ActiveUS9166610B2Improve system robustnessSimple processElectric signal transmission systemsAnalogue-digital convertersNonlinear distortionNonlinear model
The invention relates to an arrangement and a method for the identification of parameters in a nonlinear model of an analog-to-digital converter (ADC 17) and the use of this information to reduce the nonlinear distortions of the ADC. A parameter estimator determines an AD parameter vector PAD which describes the nonlinearities of the ADC (17). According to the invention the ADC is excited by a perturbed input signal y′A generated by adder 77 which combines the analog input signal yA with perturbation signal s1 provided by generator. The nonlinear system identification uses intermodulation distortion generated in the digital ADC output signal y′D which are not found in the analog input signal yA. A nonlinear AD compensation element compensates based on parameter vector PAD the nonlinear distortion generated by the ADC and generates the linearized output signal yD. Digital control information PP,1 are used to generate the perturbation signal s1 in generator and to remove the perturbation signal the compensation element. The linearized ADC (is the basis for linearizing digital-analog-converters (DAC), amplifiers and other hardware components.
Owner:KLIPPEL WOLFGANG
Identification method and device for frequency and damping ratio of main components of wind power generating set
ActiveCN108879786BImprove accuracyThe identification method is simpleSingle network parallel feeding arrangementsWind energy generationDamping ratioComputational physics
The invention discloses a method and device for identifying frequency and damping ratio of main components of wind power generator. The method comprises the following steps of: obtaining the input data and output data of the main components of a wind power generator system, and using the input and output data respectively as the input and output of a fan model established by nonlinear system identification; calculating the model parameters of the fan model and a transfer function of the main components; calculating the pole of the transfer function, obtaining a first frequency range, performing power spectrum analysis on the output data of the main components, obtaining a second frequency range, and narrowing the range of the first frequency range by using the second frequency range to obtain the frequency iteration range; calculating the amplitude corresponding to each frequency in the frequency iteration range, and obtaining the maximum amplitude, the target frequency corresponding to the maximum amplitude and the target pole p, calculating the damping ratio of the main components by formula as shown in the specification. The above technical solution disclosed in the present application can improve the accuracy of frequency and damping ratio identification of main components, and the identification method is simple and easy to operate.
Owner:ZHEJIANG WINDEY
Feedforward neural network structure self-organization method based on neuron saliency
ActiveCN107273971BImprove rationalityImprove scienceNeural architecturesNeural learning methodsEngineeringImproved algorithm
The invention discloses a self-organization method of a feedforward neural network structure based on neuron salience, relates to the technical field of neural networks, and aims at the problem of difficult selection of a traditional feedforward neural network structure, the method proposed by the invention is based on hidden layer neurons The significance size dynamically adjusts the network structure of the neural network. Experimental results show that the improved algorithm can reduce the blindness of network structure selection, realize the dynamic optimization and adjustment of network structure, and improve the accuracy of network recognition. It has high accuracy in nonlinear system identification, data classification and engineering defect type identification.
Owner:SHIJIAZHUANG TIEDAO UNIV
Identification Method of Loudspeaker Nonlinear System
ActiveCN106068007BImprove accuracyIncrease flexibilitySubsonic/sonic/ultrasonic wave measurementUsing electrical meansLinear componentNonlinear parameters
The invention provides a speaker nonlinear system identification method, comprising the following steps: providing an amplified excitation signal to the speaker system to be tested; synchronously measuring voltage signals and current signals at both ends of the speaker system; obtaining linear parameters of the speaker system: According to the measured voltage signal and the measured current signal, under large signal conditions, calculate the impedance curve of the speaker system, and use the least square method to match the impedance curve to obtain the linear parameters of the speaker system; obtain the nonlinear parameters of the speaker system : Input the measured current signal into the lumped parameter model of the loudspeaker system to calculate the estimated voltage signal; compare the estimated voltage signal with the measured voltage signal, and calculate the voltage error signal between the two; calculate the voltage error signal In decoherence, the linear component in the voltage error signal is removed, and then according to the voltage error signal after decoherence, an adaptive iterative algorithm is used to obtain nonlinear parameters.
Owner:AAC TECH PTE LTD
A Nonlinear System Identification Method Based on Multi-objective Genetic Programming
InactiveCN104616062BWide applicabilityReduce computational complexityGenetic modelsAlgorithmNonlinear system identification
The invention discloses a nonlinear system recognizing method based on multi-target genetic programming. The method mainly overcomes the shortcomings of low recognizing accuracy and low solving speed of the traditional recognizing method under the condition that the structure of a nonlinear system is unknown. A plurality of target optimization function models optimizing the structure of the nonlinear system and the system parameters are provided. A novel multi-target genetic programming method is adopted to solve the model. A novel evaluation method aiming at multiple optimization targets is provided, a decision making process is blended in the optimization process, calculation of multiple Pareto optimum solutions is not required, and the evaluation process is high in efficiency. The method can effectively excavate the nonlinear relation between a large amount of input-output data, and improve recognizing efficiency and accuracy.
Owner:HOHAI UNIV
Anti-interference compound control method of permanent magnet synchronous motor servo system based on backlash compensation
PendingCN114024473AReduce adverse effectsImprove anti-interference abilityVector control systemsDynamo-electric converter controlGear wheelPermanent magnet synchronous motor
The invention discloses an anti-interference compound control method of a permanent magnet synchronous motor servo system based on backlash compensation. The method is based on nonlinear system identification, a finite time interference observer and a sliding mode control technology, and comprises the following steps of: firstly, obtaining a motor system of a current PI control closed loop based on a vector control scheme of a permanent magnet synchronous motor; secondly, aiming at the approximate dead zone backlash nonlinear model, identifying key parameters of the backlash model by adopting a nonlinear least square iterative algorithm; thirdly, estimating gear transmission torque according to the identified backlash model; aiming at lumped disturbance existing in the system, designing a high-order sliding mode observer with finite time convergence to observe the lumped disturbance; and finally, designing a composite anti-interference controller based on the sliding mode technology in combination with estimation of gear transmission torque and lumped disturbance. According to the scheme, the influence of backlash nonlinearity and other system uncertainty and disturbance factors is effectively suppressed, the anti-interference capability is high, and the tracking performance and the steady-state precision of the system are ensured.
Owner:SOUTHEAST UNIV +1
A Nonlinear System Identification Method Based on Structure Adaptive Filtering
The invention provides a nonlinear system identification method based on structure adaptive filtering, which comprises the following steps: obtaining input data and corresponding output data of an identification object as a data set, and dividing the data set into a training set and a test set; constructing a self-adaptive filtering model: randomly selecting a first-order subsystem and a second-order subsystem according to the generation parameters, and randomly cascading the subsystems according to a connection rule to form a complete self-adaptive filtering model; initializing an evolutionary algorithm, and selecting an optimal adaptive filtering model; carrying out loop iteration through an evolutionary algorithm, and finding the optimal structure and parameters of the optimal adaptivefiltering model; and testing by using the trained adaptive filtering model to obtain the actual output of the identification object. According to the method, the nonlinear process of the typical liquid saturated steam heat exchanger is learned by utilizing the nonlinear adaptive filtering model, and the optimal estimation can be found; the method is simple in operation, small in error, high in precision and few in used parameters, and has remarkable nonlinear system recognition capability.
Owner:HENAN UNIVERSITY
A method and system for maximally long sequence optimization for nonlinear system identification
InactiveCN106580338BOverlap artifact avoidanceImprove accuracyDiagnostic signal processingAudiometeringAlgorithmEngineering
The invention discloses a maximum length sequence optimization method and system for nonlinear system identification; the system comprises an m-sequence generating unit, a displacement function calculating unit, an allowable desirable section length calculating unit, and an optimal m-sequence selecting unit. The method comprises: generating corresponding m-sequences according to the preset order of a primitive polynomial; calculating displacement function and allowable desirable section length corresponding to each m-sequence; selecting allowable desirable section length with maximum value from the corresponding allowable desirable section lengths of all the m-sequences. By using the method and system of the invention, it is possible to effectively avoid overlap distortion possibly occurring during calculation of core sectioning, and identification accuracy is greatly improved. The maximum length sequence optimization method and system for nonlinear system identification are widely applicable to the field of biomedical signal treatment.
Owner:SOUTHERN MEDICAL UNIVERSITY
Identification Method of Wiener Nonlinear System Based on Parameter Separation
ActiveCN111399477BReduce computationHigh precisionProgramme controlElectric testing/monitoringComputational physicsNonlinear system identification
The invention discloses a method for identifying a Wiener nonlinear system based on parameter separation, comprising the following steps: A. converting a thermal power plant control system to be identified into a Wiener nonlinear system, and performing an input quantity of the thermal power plant control system to be identified Merge; B. Analyze the Wiener nonlinear system, including the linear dynamic part structure of the system, the nonlinear static part structure of the system, the dynamic disturbance type, and the measurement noise; determine n a , n b and n c , set the initial value, and collect the input data u(k) and y(k) repeatedly until k≥n a +n, k≥n b +n; C. Separate the time-invariant parameters and time-varying parameters of the Wiener nonlinear system; D. Identify the Wiener nonlinear system; E. When the identified model does not meet the requirements, return to step A and readjust the model structure and initial values, re-identify until a system model that meets the requirements is obtained. The invention can improve the deficiencies of the prior art, and improves the identification accuracy and convergence speed of the Wiener nonlinear system.
Owner:NORTH CHINA INST OF AEROSPACE ENG
Nonlinear system identification method based on tensor product network B spline
PendingCN113779780AProve efficiencyProof of accuracyDesign optimisation/simulationSpecial data processing applicationsNumerical stabilityNonlinear dynamical systems
The invention discloses a nonlinear system identification method based on a tensor product B-spline, which comprises the following steps: firstly describing a non-linear dynamic system by adopting a non-linear autoregression model NARX, then identifying the NARX model based on a tensor product network B-spline, and identifying the non-linear dynamic system by adopting an iterative method based on an alternating linear format; directly estimating a low-rank tensor network used for approaching a high-dimensional B spline to eliminate the need for explicitly constructing an index multiple weight tensor; finally, ensuring the numerical stability by orthogonality of the TT kernel so that the robustness to noise is achieved, monotonic convergence is ensured, and a robust identification result of a nonlinear system is obtained. The efficiency and accuracy of the identification method are proved through numerical experiments on a nonlinear system.
Owner:NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
A Simple Grey Box Space Identification Method for Hammerstein Nonlinear Industrial Systems Driven by Dynamic and Static Data
The invention relates to a simple grey-box space identification method for a Hammerstein nonlinear industrial system driven by dynamic and static data, and belongs to the field of control theory and control engineering nonlinear system identification. The method includes: S1: collect dynamic data and static data of the system; S2: select and process the Hammerstein nonlinear system model to obtain a prediction model; S3: construct a dynamic parsimonious model of the Hammerstein nonlinear system; S4: construct a static parsimonious model of the Hammerstein nonlinear system ; S5: Use the hierarchical Lagrangian optimal weighting method to solve the system parameter fusion identification. The present invention simultaneously adopts dynamic and static data, based on a decomposed parsimonious model, avoids the estimation of additional intermediate parameters, reduces the variance of the estimated model, and improves the accuracy of the model.
Owner:CHONGQING UNIV OF POSTS & TELECOMM
Method and device for identifying frequency and damping ratio of main components of wind power generator
ActiveCN108879786AImprove accuracyThe identification method is simpleSingle network parallel feeding arrangementsWind energy generationModel parametersDamping ratio
The invention discloses a method and device for identifying frequency and damping ratio of main components of wind power generator. The method comprises the following steps of: obtaining the input data and output data of the main components of a wind power generator system, and using the input and output data respectively as the input and output of a fan model established by nonlinear system identification; calculating the model parameters of the fan model and a transfer function of the main components; calculating the pole of the transfer function, obtaining a first frequency range, performing power spectrum analysis on the output data of the main components, obtaining a second frequency range, and narrowing the range of the first frequency range by using the second frequency range to obtain the frequency iteration range; calculating the amplitude corresponding to each frequency in the frequency iteration range, and obtaining the maximum amplitude, the target frequency corresponding to the maximum amplitude and the target pole p, calculating the damping ratio of the main components by formula as shown in the specification. The above technical solution disclosed in the present application can improve the accuracy of frequency and damping ratio identification of main components, and the identification method is simple and easy to operate.
Owner:ZHEJIANG WINDEY
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