33 results about "Nonlinear system identification" patented technology

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 P_AD 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 y_A 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 y_A. A nonlinear AD compensation element compensates based on parameter vector P_AD the nonlinear distortion generated by the ADC and generates the linearized output signal y_D. Digital control information P_P,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.

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.

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.

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.

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.

The invention discloses a Wiener nonlinear system identification method based on parameter separation. The Wiener nonlinear system identification method comprises the following steps of: A, convertinga to-be-identified thermal power plant control system into a Wiener nonlinear system, and combining input quantities of the to-be-identified thermal power plant control system; B, analyzing the Wiener nonlinear system, wherein the Wiener nonlinear system comprises a system linear dynamic part structure, a system nonlinear static part structure, a dynamic interference type and measurement noise, determining na, nb and nc, setting an initial value, and repeatedly collecting input data u(k) and y(k) until k is greater than or equal to na+n and k is greater than or equal to nb+n; C, separating atime-invariant parameter and a time-variant parameter of the Wiener nonlinear system; D, identifying the Wiener nonlinear system; E, and returning to the step A when the identified model does not meetthe requirements, re-adjusting the structure and the initial value of the model, and re-identifying the system until the system model meeting the requirements is obtained. According to the Wiener nonlinear system identification method, the defects in the prior art can be overcome, and the precision and convergence rate of Wiener nonlinear system identification are improved.

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.

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.