27 results about "Volterra series" patented technology

The invention discloses a neural network-based Wollaston series nonlinear compensation method, and the method specifically comprises the steps: enabling a Wollaston integral kernel to be used for initializing the weight of a neural network, and achieving the accurate and efficient nonlinear compensation through training; firstly, a transmission experiment is carried out based on an optical fiber transmission experiment platform, and experiment data are collected from a real-time oscilloscope to construct a training set; and then calculating a Wollaston integral kernel according to the link parameters, applying the Wollaston integral kernel to weight initialization of the neural network, and finally performing iterative updating on the weight parameters of the neural network by using the training data so as to improve the nonlinear compensation performance. Compared with a digital back propagation algorithm, a large number of iteration steps are omitted, single-step compensation is achieved, and the calculation efficiency is greatly improved. In a 12,000 km transmission experiment, the compensation performance superior to that of a digital back propagation algorithm is realized with lower calculation complexity and calculation time delay.

The invention discloses an electroencephalogram signal artifact removal method and an acquisition system. The method comprises the following steps: synchronously acquiring sensor data of a testee and each lead electroencephalogram signal by utilizing a unified trigger signal; performing timestamp alignment verification on the two types of synchronous data; a motion state quantitative index is determined, and if the motion state quantitative index does not exceed a preset motion threshold value, the collected electroencephalogram signals are output; if yes, an exception processing flow is triggered and comprises the steps that self-adaptive filtering and nonlinear compensation are sequentially executed on the electroencephalogram signals, static electroencephalogram data of the testee serve as reference in the self-adaptive filtering process, an NLMS filter with the step length dynamically adjusted is adopted, a motion artifact linear part and base noise are restrained, a self-adaptive filter is constructed based on the second-order Volterra series in the nonlinear compensation process, and the motion artifact linear part and the base noise are restrained; a kernel coefficient is optimized through an RLS algorithm, and nonlinear distortion is processed; and outputting the electroencephalogram signal after the exception processing. The method is suitable for non-linear and non-stationary motion artifact signals, signal synchronism and quality can be improved, and processing efficiency is considered.

The invention discloses a high-precision dynamic error real-time compensation method based on five-axis linkage, and relates to the technical field of machine manufacturing, and the method comprises the following steps: multi-source dynamic error perception: collecting geometric errors of a machine tool through a grating ruler, collecting temperature field distribution through an infrared thermal imager, and collecting cutting force disturbance through a piezoelectric force sensor; the method comprises the steps of constructing a grating ruler, constructing a thermal infrared imager, constructing a multi-source data acquisition system, carrying out nonlinear coupling modeling, and establishing a dynamic error model based on improved Volterra series, and has the advantages that the multi-source data acquisition system is constructed through the grating ruler, the thermal infrared imager and the piezoelectric force sensor, geometric, thermal and force multi-source dynamic errors are sensed synchronously, and the dynamic error detection accuracy is improved. The nonlinear coupling characteristic of the thermal-force-geometric error is accurately quantified by combining an improved Volterra series model and Lyapunov exponent analysis, the problem that a traditional single-source error model cannot accurately describe a complex error field is solved, and the accuracy of error prediction is remarkably improved.

There is provided mechanisms for operating a DPD for a non-linear electronic device. A method is performed by a DPD controller. The method comprises receiving an input signal destined to be input to the non-linear electronic device. The method comprises selecting a basis function that represent non-linear input-output characteristics of the non-linear electronic device. The basis function is defined by kernels of a pruned Volterra series, and comprises higher-than-order-1 polynomial terms, polynomial cross-terms, memory cross-terms, and common tap delays shared by all polynomial terms. The method comprises obtaining an output signal by subjecting the input signal to a linearization function defined by the basis function. The method comprises providing the output signal as input to the non-linear electronic device.

The invention discloses a modeling method of a radio frequency MOS (Metal Oxide Semiconductor) device, and relates to the technical field of MOS device modeling. Electromagnetic simulation and thermal field simulation are combined, a dynamic parasitic parameter library is established, parasitic effects under complex working conditions are described, the dynamic parasitic parameter library is taken as a core basis, the dynamic response capability of a model is remarkably improved, and the modeling efficiency of the radio frequency MOS device is improved. Dynamic parasitic parameters are introduced into a small-signal equivalent circuit model, dynamic changes of the parasitic parameters are explicitly embodied, a partition modeling method is introduced, dynamic behaviors of a grid electrode, a source electrode and a drain electrode are refined into independent contributions, local dynamic characteristics of the radio frequency MOS device can be described more accurately, modeling precision is enhanced, large-signal excitation and nonlinear polynomial description are introduced, and the dynamic characteristics of the radio frequency MOS device can be described more accurately. The method comprises the steps of capturing nonlinear behaviors under a high-power condition, establishing a unified behavior model from linearity to nonlinearity, accurately describing high-order nonlinearity by utilizing a Volterra series method, and solving key problems of signal frequency aliasing and dynamic distortion in nonlinear analysis in combination with dynamic parasitic parameters.

The invention relates to the technical field of coherent light transmission, and discloses a nonlinear equalization method of an ultra-high-order QAM coherent light transmission system, which comprises the following steps: acquiring a damaged digital signal after a signal to be transmitted is transmitted; iQ orthogonalization is carried out on the X polarization state and the Y polarization state of the damaged digital signal, in-phase components and orthogonal components of the X polarization state and the Y polarization state are obtained and expressed as XI, XQ, YI and YQ, and optimized in-phase components and optimized orthogonal components of the X polarization state and the Y polarization state are obtained through dispersion compensation, clock recovery, multimode operation, frequency offset estimation and carrier phase recovery in sequence; for the X polarization state and the Y polarization state, the optimized in-phase component and the optimized orthogonal component are used as input, an improved Wollaston polynomial equalizer is input, combined equalization is carried out through Wollaston series, corresponding X polarization state estimation digital signals and Y polarization state estimation digital signals are output, and equalization digital signals are obtained through fusion. And completing nonlinear equalization of the damaged digital signal.

The invention belongs to the technical field related to robot machining dynamics, and discloses a robot milling system tail end nonlinear frequency response modeling method and device and application, and the method comprises the steps: S1, building a robot tail end nonlinear frequency response model through experimental analysis; s2, linear kinetic parameters and nonlinear kinetic parameters of the nonlinear frequency response model are identified through a harmonic detection method, and nonlinear response of the robot milling system is analyzed based on Volterra series; and S3, identifying parameters of a joint part of the main shaft and the cutter handle based on the obtained kinetic parameters, and predicting a force / displacement frequency response function of the cutter point of the robot milling system in combination with an RCSA theory so as to realize modeling of nonlinear frequency response of the cutter point of the robot milling system. The machining time cost and the economic cost are reduced.

The nonlinearity of power amplifiers (PAs) has been a severe constraint in performance of modern wireless transceivers. This problem is even more challenging for the fifth generation (5G) cellular system since 5G signals have extremely high peak to average power ratio. Nonlinear equalizers that exploit both deep neural networks (DNNs) and Volterra series models are provided to mitigate PA nonlinear distortions. The DNN equalizer architecture consists of multiple convolutional layers. The input features are designed according to the Volterra series model of nonlinear PAs. This enables the DNN equalizer to effectively mitigate nonlinear PA distortions while avoiding over-fitting under limited training data. The non-linear equalizers demonstrate superior performance over conventional nonlinear equalization approaches.

The invention discloses a vehicle model nonlinear characteristic analysis and quantification method based on Volterra series, and the method comprises the following steps: building a vehicle dynamics model, and describing the basic motion characteristics of a vehicle; the transformation model is a polynomial nonlinear expression mode suitable for Volterra series analysis; calculating a kernel function of the kinetic model by adopting a harmonic detection method; a frequency multiplication signal transfer function is extracted, and the nonlinear degree is quantized; and the analytic expression of the signal transmission characteristic and the nonlinear degree of the vehicle under different driving states and environment information is obtained. Different driving parameters and environment information are introduced into the calculated kernel function, the signal transmission characteristic and the non-linear degree under the corresponding working condition can be calculated, and the calculation result can be used for effectively guiding controller design.

The application discloses an electroencephalogram artifact removal method and a collection system. The method uses a unified trigger signal to synchronously collect sensor data and electroencephalogram signals of a subject. Time stamp alignment verification is performed on the two types of synchronous data. A motion state quantitative index is determined. If the index does not exceed a preset motion threshold, the collected electroencephalogram signals are output. If the index exceeds the threshold, an abnormal processing procedure is triggered. The procedure includes sequentially performing adaptive filtering and nonlinear compensation on the electroencephalogram signals. The adaptive filtering takes the subject's resting electroencephalogram data as a reference, uses a step-dynamically-adjusted NLMS filter to suppress the linear part of the motion artifact and the baseline noise, and the nonlinear compensation constructs an adaptive filter based on a second-order Volterra series, optimizes kernel coefficients through an RLS algorithm, and processes nonlinear distortion. The output is the electroencephalogram signals after abnormal processing. The method is suitable for nonlinear and non-stationary motion artifact signals, can improve signal synchronicity and quality, and takes into account processing efficiency.

A coherent channel pre-equalization optimization method, apparatus and device based on double real value input Volterra series, and a computer readable storage medium, comprising: separating an output signal obtained by processing an input signal by an obtained target channel to obtain a real part corresponding to an I path and an imaginary part corresponding to a Q path in the output signal, the real part corresponding to the I path and the imaginary part corresponding to the Q path are double real values; inputting the real part corresponding to the I path and the imaginary part corresponding to the Q path into a preset equalizer so as to optimize the preset equalizer, and obtaining an optimized equalizer; and inputting the input signal into the optimized equalizer, and obtaining a target output signal output by the optimized equalizer so as to complete pre-equalization of the transmitting end, thereby solving the technical problem that a traditional single-input real value equalization scheme in related technologies is not accurate enough in signal distortion compensation, and improving the accuracy of channel transmission.

This application relates to the field of data processing technology and discloses an electrical performance testing platform and method. The platform includes: a modeling module for digitally modeling a target circuit to obtain a digital circuit model, and performing finite element analysis on the digital circuit model based on preset operating condition parameters to obtain time-domain analog signal data and thermal effect data; a transformation module for performing short-time Fourier transform on the time-domain analog signal data to obtain a two-dimensional circuit spectrum, and performing distribution mapping on the thermal effect data to obtain a thermal effect distribution map; an extraction module for extracting response features from the two-dimensional circuit spectrum using discrete Wolterra series to obtain a response feature set, and extracting features from the thermal effect distribution map to obtain a thermal effect feature set; and a classification module for classifying fault types using a hidden Markov model to obtain potential fault types and state transition probabilities. This application improves the accuracy and efficiency of electrical performance testing of circuits.

A digital predistortion module (DPD) (300) for linearization of a non-linear electronic device is disclosed. The DPD module (300) comprises an input module (310) configured to receive an input signal x(n) and a function module (320) defined by a pruned Volterra series with memory polynomial terms, wherein the function module comprises a set of basis function components Fj (p ( j ) 1, q ( j ) 1 p2'> > q2 (i) I A ( i ), D ( j ) 1, Di'> > Ds >, D ( j ) 4, x(n)). The polynomial order and time delay of the function module (320) are determined or calculated by algorithm based on the input signal x(n), and an error signal e(n) representing a difference between the input signal x(n) and an output signal z(n) of the non-linear electronic device. The DPD module (300) further comprises an output module (330) configured to combine outputs from the set of basic function components to output an output signal y(n).

The invention relates to an iterative estimation method for quickly identifying model parameters of a single-phase photovoltaic grid-connected inverter which is interfered by colored noise and has information loss. With the promotion of intelligent power grid construction, stable and efficient operation of a power grid is significant. The performance of single-phase power equipment serving as a power grid basic unit directly affects the electric energy quality and the power supply reliability. However, equipment is often interfered by noise in actual operation, information loss exists due to the problems of sensor faults, communication interruption and the like, and modeling and identification are difficult. According to the method, a double-rate Volterra-Wiener model of a single-phase photovoltaic grid-connected inverter based on a Volterra sequence is taken as an object, and a three-stage Levenberg-Marquardt iterative algorithm based on filtering is provided. Colored noise is eliminated through data filtering, and the system is decomposed into three subsystems by using a hierarchical principle. The Levenberg-Marquardt algorithm is insensitive to initial value selection, the convergence speed is high, and the method aims at achieving rapid and effective identification of model parameters which are interfered by colored noise and have information loss.

A distortion apparatus for a power amplifier comprises: pre-distortion circuitry that comprises a memory polynomial look-up-table (LUT) circuit; and post-distortion circuitry that updates the pre-distortion circuitry based on an output of the power amplifier. The pre-distortion circuitry comprises a plurality of LUTs. Each LUT corresponds to a different memory depth of a memory polynomial of a Volterra series. Each LUT indexed by an instantaneous power of an input sample delayed by an amount corresponds to a respective memory depth of each LUT. An output of the memory polynomial LUT circuit corresponds to a summation of an output of each LUT multiplied by the input sample delayed by the amount corresponding to the respective memory depth of each LUT, and an output of the pre-distortion circuitry is provided to the power amplifier.

A communications apparatus to resolve a composite signal including an induced nonlinear distortion, a desired signal and interferer signals, wherein the desired signal includes desired symbols and the interferer signals include interferer symbols using N frameworks, each framework including a detector to partition the desired symbols and the interferer symbols based on an interference severity into a dominant group and a non-dominant group, and to generate A Posteriori Probabilities (APP) of the desired symbols and the interferer symbols, wherein the detector of each of the N frameworks generates the APP based on a feedback of the APP from each of the N frameworks, and the detector reduces the induced nonlinear distortion of the desired signal using a nonlinear mathematical model such as Volterra series.

This invention discloses a high-precision dynamic error real-time compensation method based on five-axis linkage. This invention relates to the field of mechanical manufacturing technology and includes the following steps: multi-source dynamic error sensing: A multi-source data acquisition system is constructed by collecting machine tool geometric errors using a grating ruler, temperature field distribution using an infrared thermal imager, and cutting force disturbances using a piezoelectric sensor. Nonlinear coupling modeling is then performed, and a dynamic error model is established based on an improved Volterra series. The advantages of this invention are: by constructing a multi-source data acquisition system using a grating ruler, infrared thermal imager, and piezoelectric sensor, geometric, thermal, and force multi-source dynamic errors are simultaneously sensed. Combined with the improved Volterra series model and Lyapunov exponent analysis, the nonlinear coupling characteristics of thermal-force-geometric errors are accurately quantified, solving the problem of inaccurate description of complex error fields by traditional single-source error models and significantly improving the accuracy of error prediction.

This invention discloses a distributed photovoltaic (PV) power quality assessment method based on an improved Volterra series and adaptive interval type-two fuzzy logic. The method involves acquiring and preprocessing raw voltage signals from key nodes of the distributed PV system to obtain real-time PV operating parameters, calculating a light intensity weighting factor, and then calculating the Volterra output and extracting core features based on the light intensity weighting factor, the preprocessed voltage signals, and pre-calculated first-order and symmetric second-order kernels. These core features are mapped to IT2FS, and the Mamdani inference method is used to obtain the IT2FS inference results from a PV power quality rule base. The centroid method is then used to reduce the type of perturbation and its severity level for the core features. The source of the perturbation is determined based on the feature threshold and the core feature value. This invention achieves accurate assessment of the type, severity level, and source of power quality perturbations in distributed PV systems.

The present invention discloses a modeling method for radio frequency MOS devices, and relates to the technical field of MOS device modeling. The present invention combines electromagnetic simulation with thermal field simulation to establish a dynamic parasitic parameter library to describe parasitic effects under complex working conditions. With the dynamic parasitic parameter library as the core foundation, the dynamic response capability of the model is significantly improved, and the dynamic parasitic parameters are introduced into a small signal equivalent circuit model to explicitly reflect the dynamic changes of the parasitic parameters. A partitioning modeling method is introduced to refine the dynamic behaviors of the gate, source, and drain into independent contributions, which can more accurately describe the local dynamic characteristics of the radio frequency MOS device and enhance the modeling accuracy. Large signal excitation and nonlinear polynomial description are introduced to capture the nonlinear behavior under high power conditions, and a unified behavioral model from linear to nonlinear is established. The Volterra series method is used to accurately describe high-order nonlinearity. Combined with the dynamic parasitic parameters, the key problems of signal frequency aliasing and dynamic distortion in nonlinear analysis are solved.

The present invention relates to the fields of hydrology and water resource, water conservancy and hydropower engineering, and emergency disaster prevention. Disclosed is a flood forecasting error cor

The invention provides an underground cable three-dimensional image detection method based on terahertz data, and the method comprises the steps: obtaining the terahertz data of an underground cable, and generating the dielectric constant distribution of the underground cable according to the terahertz data through a multi-physics coupling waveguide model; compressing the dielectric constant distribution into a complex signal by using a quantum derivative compressed sensing algorithm, extracting an electric field time-domain signal from the complex signal, and performing nonlinear phase compensation on the electric field time-domain signal by using a preset Volterra series model to obtain a compensated electric field time-domain signal; performing tensor decomposition on the compensated electric field time domain signal to obtain a feature matrix, and performing Bayesian inversion on the feature matrix to obtain optimal cable parameters of the underground cable; and generating an optimal three-dimensional parameter field according to the optimal cable parameters, and drawing a three-dimensional image of the underground cable according to the optimal three-dimensional parameter field. In this way, the problem that existing metal detection, low-frequency electromagnetic waves and radar imaging are limited in penetrating power and resolution in an underground complex environment is solved.

The present invention belongs to the field of communication technology and specifically discloses a compensation method, compensator, and system for nonlinear distortion of a pulsed field source. The compensation method comprises: constructing a Volterra series model for simulating the nonlinear distortion of a digital receiver, and loading the signal to be compensated received by the digital receiver into the Volterra series model to obtain a distorted signal, wherein the distorted signal carries a nonlinear distortion amount, and constructing a compensation model for characterizing the nonlinear distortion amount; using the compensation model to eliminate the nonlinear distortion amount in the distorted signal to obtain a compensation output; based on the compensation output, using the least squares method to update the compensation kernel vector of the compensation model, so as to eliminate the nonlinear distortion amount in the distorted signal in real time using the compensation model after the compensation kernel vector is updated. The present invention solves the problem of nonlinear distortion of a pulsed field source and the difficulty of directly compensating for the nonlinearity of the receiver without adding an additional analog-to-digital converter (ADC).

The invention discloses a lightweight multi-heat-source thermal model construction method and system for an integrated microsystem, and the method comprises the following steps: S1, constructing a nonlinear material attribute incidence matrix Mmat through self-adaptive analysis of a grid file, extraction of a topological relation between tetrahedral elements, node coordinates and surface elements; s2, setting a transient heat source and a heat exchange boundary according to a tetrahedron unit space relationship, assembling a unit matrix based on an Mmat matrix, and integrating a system matrix; s3, substituting the system matrix into a preset frequency point to solve a Volterra series response matrix of the system, and performing singular value decomposition on the response matrix to generate an orthogonal temperature rise field base and a nonlinear auxiliary field base lambda s (r); s4, decoupling a nonlinear term mu of the temperature rise field by using an auxiliary field base lambda s (r), and realizing time domain solution of the transient nonlinear temperature rise field through an alternating iteration mechanism of the temperature rise field and an auxiliary field in a time domain; and S5, decoupling the multi-heat-source power density function into independent mapping through temperature rise field base superposition, and realizing lightweight multi-heat-source thermal model construction of the integrated microsystem.

The invention discloses a TIADC system dynamic nonlinear error estimation and correction method based on Volterra series, and the method comprises the steps: obtaining a digital signal y (n) after an input signal passes through a TIADC system, generating a pseudo ideal signal # imgabs0 # through a feature value extracted from the digital signal y (n), and solving a least square solution through a formula of a nonlinear structure and matrix transformation. Obtaining a dynamic nonlinear error coefficient estimation value # imgabs2 # estimated by a dynamic nonlinear error coefficient estimation value # imgabs1 #; obtaining a dynamic nonlinear error estimation value, namely an error # imgabs3 #, through a digital reconstruction module according to the output approximation of the system; then, carrying out digital correction processing; according to the dynamic nonlinear error correction method, the effect of the correction method is well maintained while the complexity of a nonlinear model is reduced, and meanwhile, the correction process does not contain any complex number operation and can be achieved only through a multiplier and an adder.

The invention provides a nonlinear system voice noise reduction method and system based on a maximum entropy criterion, and belongs to the technical field of voice communication active noise elimination, and the method comprises the steps: obtaining a noise source signal discrete value; performing Volterra series expansion on the signal discrete value to obtain a first input signal; decomposing the tap vector of the adaptive filter by using a Kronecker integral solution to obtain a plurality of decomposition filters; obtaining an error signal based on the first input signal and a decomposition filter; an error signal is collected and fed back to the filter, a tap weight vector of the decomposition filter at the next moment is obtained, and a maximum entropy criterion is adopted to update a weight vector of the adaptive filter; and n is updated to the next moment value until the noise reduction process is finished. According to the invention, the calculation complexity is reduced, and the signal-to-noise ratio of the noise reduction signal is improved.

The present invention provides a high-speed optical communication system equalization method. The method constructs a neural network equalizer based on Volterra series, adopts mutual correlation learning to enhance a joint neural network, combines linear and nonlinear compensation, eliminates the need for an additional linear compensation module in the network, and enhances the overall compensation capability of the network. Through an additional enhanced mutual correlation learning layer, the neural network can better learn the second-order and third-order mutual correlation terms of the input signal, thereby strengthening the neural network's compensation capability for system nonlinearity. Through a high-order fully connected layer, high-order mutual correlation terms of the input signal are learned from low-order autocorrelation input signals. At the same time, a clustering algorithm is used in densely weighted areas in the network to reduce network complexity.

The invention relates to the technical field of servo control, in particular to an anti-interference capability evaluation method for a nonlinear control system, and the method comprises the steps: converting the output of a nonlinear dynamic control system into Volterra series; performing finite order truncation on the converted output; converting the output after finite order truncation to a frequency domain to obtain a generalized frequency response function; and continuously adjusting the amplitude of the excitation signal until the range of the function cluster of the generalized frequency response function does not change any more, recording the stabilization time and the stabilization error of the function cluster of the generalized frequency response function at the moment, and completing the anti-interference capability evaluation of the nonlinear dynamic control system. According to the method, the generalized frequency response function of the nonlinear algorithm is analyzed based on the truncated Volterra series, the curve cluster of the frequency response function of the anti-interference isolation of the nonlinear control system is obtained, and a more comprehensive system anti-interference capability evaluation result is obtained.