170 results about "Back propagation neural network" patented technology

The invention relates to the technical field of structural engineering, and provides a surface crack fatigue propagation morphology and life prediction method based on machine learning, which comprises the following steps: establishing a finite element simulation model containing a surface crack, calculating a stress intensity factor of the front edge of the surface crack, and generating a finite element simulation data set; performing dimensionless processing on parameters in the simulation data set to generate a machine learning data set; optimizing the back propagation neural network through a hybrid optimization algorithm, and constructing a surface crack stress intensity factor prediction model; the surface crack stress intensity factor prediction model is combined with a fatigue crack growth rate formula to cyclically predict the surface crack growth morphology and the service life of the load bearing structure; establishing a visual interaction interface of parameter input and output and image output based on the surface crack stress intensity factor prediction model and the fatigue crack growth rate formula; a user can quickly predict the propagation morphology of the surface crack and calculate the service life without mathematical analysis and code writing.

The invention relates to the technical field of data processing, and provides a resistance line-associated underwater bench blasting numerical simulation error correction method, which is characterized in that land and underwater measured data are matched through the same resistance line and similar material parameters, and the resistance line and the water depth are used as basic input of an error prediction model; a reference basis is provided for error correction of subsequent numerical simulation; calculating a target result in combination with a prediction error after initial simulation, comparing a simulation result with the target result during iterative simulation to generate an iterative error, and analyzing and determining sensitivity coefficients of different error classifications in a resistance line and the iterative error so as to adjust material parameters to reduce an invalid iterative process; simulation results are processed according to stress areas in a zoning mode through an equidistant slicing method, adjustment precision is verified in combination with the root rate and the damage area, and the reliability of the simulation results is improved; and meanwhile, parameter data passing verification are input into a back propagation neural network, a correction parameter mapping library is constructed, and the design efficiency of an underwater blasting scheme is improved.

The invention combines the advantages of priori knowledge fusion, provides an accidental explosion concrete penetration depth prediction method fused with experience algorithm knowledge, and aims to improve the prediction precision and physical rationality in a small sample scene. The method comprises the following steps: firstly, constructing a combined parameter Z through a physical parameter, a target attribute and a Forrest formula, and taking the combined parameter Z as an input feature of a model to represent a physical rule; then, a multi-objective loss function is designed, the multi-objective loss function comprises a data fitting item, a physical constraint item and a boundary constraint item, the physical constraint item verifies the physical consistency of predicted values through trace disturbance input parameters, and the boundary constraint item ensures that the penetration depth is non-negative. And further constructing a dynamic weight adjustment mechanism, and automatically balancing the optimal weight of the physical law and data fitting according to the training process. And based on a back propagation neural network framework, performing model training in combination with the constraints. According to the method, the mean square error is remarkably reduced in a concrete penetration task, and meanwhile, the physical interpretability of a data-driven model is expanded.

The invention relates to an indoor cross-scene multi-band sub-terahertz channel prediction method, a storage medium and software, and belongs to the technical field of wireless communication. In order to solve the problems of poor adaptability to nonlinear features, high calculation complexity and insufficient generalization ability in traditional channel modeling, a channel prediction model is constructed based on a back propagation neural network, and a particle swarm optimization algorithm is utilized to optimize a network initial weight and a threshold value. According to the method, system parameters and environment characteristics are fused, an indoor sub-terahertz channel simulation data set is constructed, and the indoor sub-terahertz channel simulation data set is subjected to normalization processing and then used for model training and verification. And finally, evaluating the prediction performance according to indexes such as a root-mean-square error, a mean absolute error and a decision coefficient. The method can improve the accuracy and generalization ability of channel characteristic prediction, is suitable for various indoor scenes, and provides technical support for 6G communication system deployment.

The invention provides an oil field productivity monitoring method using a carbon quantum dot tracer agent, and the method comprises the steps: S100, obtaining the convection term and diffusion term characteristics of an oil field through the injection of the carbon quantum dot tracer agent and an inert tracer agent, and building an oil field monitoring characteristic set; step S200, selecting main characteristics of oil field monitoring from the oil field monitoring characteristic set by using a Spearman correlation coefficient and a random forest tree model method; and S300, inputting the main features into a neural network to monitor the productivity of the oil field, wherein the neural network is a particle swarm optimization-back propagation neural network for Wiener process optimization.

The invention provides a gear peeling time-varying meshing stiffness prediction method and system based on a back propagation neural network, and the method comprises the steps: considering a tooth surface peeling fault based on a gear tooth bearing contact analysis method, and constructing a helical gear pair time-varying meshing stiffness calculation model; a real irregular tooth surface peeling area is fitted by adopting a least square ellipse fitting method to obtain an ellipse appearance representation, any peeling position is completely described through six key geometric parameters, the geometric parameters of the ellipse appearance are systematically traversed and fitted, and diversified peeling appearance samples are generated. Introducing a tooth profile deviation matrix corresponding to the peeling morphology sample into a tooth surface bearing contact analysis model, and constructing a training data set; and constructing a back propagation neural network model of multiple hidden layers, and performing end-to-end training by using the training data set, so that the back propagation neural network model learns a nonlinear mapping relationship from geometric parameters to a time-varying meshing stiffness curve, thereby predicting the time-varying meshing stiffness under any peeling morphology.

The invention provides an intelligent risk screening system and method based on distribution network load prediction, and relates to the technical field of intelligent power grids, and the method comprises the steps: carrying out the multi-scale decomposition processing and bidirectional feature coding of historical load data of a distribution network, and obtaining a time sequence coding feature; graph structure features are extracted from the multi-node load graph of the distribution network, feature fusion is carried out on the graph structure features and the time sequence coding features, and a multi-dimensional prediction matrix is obtained; performing a back propagation neural network model optimization operation based on genetic algorithm learning on the multi-dimensional prediction matrix through the environmental influence data to obtain a load prediction curve and a prediction confidence coefficient, and determining overload risk early warning of the distribution network load according to the load prediction curve and a preset risk threshold value; according to the method and the device, accurate screening of the overload risk of the distribution network based on space-time modeling and a prediction confidence mechanism can be realized, so that the accuracy of load early warning is improved.

The invention provides a gearbox variable working condition mechanical loss torque efficiency correction test method based on a neural network. The method comprises the following steps: S1, determining a to-be-tested gearbox and basic parameters; s2, building a gearbox variable working condition test system; s3, acquiring basic operation data of the gearbox; s4, calculating theoretical mechanical loss torque and efficiency of the gear box; s5, the actually-measured mechanical loss torque and efficiency of the gearbox are collected, and an error data set is generated; s6, constructing and training an error correction model fusing the back propagation neural network and support vector regression; s7, performing mechanical loss torque and efficiency correction based on the fusion model; and S8, verification and feedback optimization of a correction result. According to the method, on the basis of all-working-condition data, the defect of a single model is overcome through double-algorithm fusion, precise correction of the mechanical loss torque and efficiency of the gearbox under the variable working conditions is achieved, and reliable data support is provided for gearbox design optimization, working condition matching and energy efficiency evaluation.

The invention relates to the technical field of power electronics, in particular to an impedance identification method and system for a doubly-fed fan grid-connected system based on data driving, and the method specifically comprises the steps: determining the input and output of the system based on a dq theoretical impedance model of the doubly-fed fan grid-connected system; building a time domain simulation model of the single doubly-fed fan grid-connected system, and performing data collection under various operation conditions by using a frequency sweep method; constructing a single doubly-fed fan impedance identification model based on a back propagation neural network optimized by a genetic algorithm, training the single doubly-fed fan impedance identification model, aggregating the station impedance identification model by using the trained single doubly-fed fan impedance identification model, and then performing impedance identification to obtain equivalent impedance after aggregation of the wind power station; and analyzing the stability of the doubly-fed fan grid-connected system by using the impedance identification model based on the generalized Nyquist criterion. According to the method, the impedance identification precision is ensured, and the impedance identification speed is greatly increased at the same time.

The invention provides an airport noise prediction method based on a back propagation neural network and a support vector machine, and belongs to the technical field of air traffic transportation noise, and the method comprises the steps: collecting airport noise historical data, obtaining flight historical data of airplanes with different powers, and obtaining a prediction input variable and a prediction output variable; according to the input and output variables and an elastic back propagation algorithm, in combination with a gradient time sequence dynamic rarefaction method and an adaptive structure adjustment method, establishing a back propagation neural network prediction model; constructing an airport noise kernel function, and establishing a support vector machine prediction model in combination with a cross validation method; predicting by using a back propagation neural network prediction model and a support vector machine prediction model, comparing and analyzing prediction results in combination with evaluation indexes, and selecting an optimal prediction result as an airport noise prediction result; the problems that an existing noise prediction algorithm depends on a large amount of historical data, the calculated amount is huge, and the noise prediction efficiency is low are solved.

The application discloses a battery thermal runaway early warning method, device and equipment of energy storage power station and medium, using neural network to predict the battery thermal runaway risk of energy storage power station, early warning before the battery thermal runaway of energy storage power station, improve the safety.In addition, first train the probabilistic neural network with a small amount of training samples, and after the convergence of the probabilistic neural network, train the back propagation neural network with the soft label output by the probabilistic neural network, solve the problem of slow learning speed and insufficient training samples in the initial training of the back propagation neural network, and improve the model training efficiency.At the same time, the soft label can solve the problem that the back propagation neural network is easy to fall into local minimum value in the training process.

The invention discloses a time sequence reconstruction and machine learning coupled chlorophyll monitoring data breakpoint restoration method, and belongs to the technical field of water quality monitoring. The invention discloses a chlorophyll monitoring data breakpoint restoration method based on coupling of time sequence reconstruction and machine learning, and the method comprises the following steps: S1, collecting water quality monitoring data, and cleaning the monitoring data to obtain preprocessed data; s2, performing time sequence reconstruction on the preprocessed data to obtain a weekly average 1 data set; s3, respectively constructing a radial basis function neural network model and a back propagation neural network model by taking the chlorophyll concentration as a response variable and the conventional water quality parameter as a predictive variable; s4, performing performance evaluation on each model by taking a root mean square error, an average absolute percentage error, goodness of fit and relative error distribution statistics as evaluation indexes, and screening out an optimal model; and S5, applying the conventional water quality parameters in the breakpoint interval of the chlorophyll monitoring data in the water body to the optimal model, and outputting the restored chlorophyll concentration value to complete the dynamic restoration of the breakpoint.

The invention discloses a wind and light power station energy storage capacity configuration method and system, and the method comprises the steps: carrying out the wind power prediction based on the historical related data of each wind power plant through employing a model based on a convolutional neural network and a long-short-term memory network; based on the historical related data of each photovoltaic power station, using a back propagation neural network model based on a genetic algorithm to carry out photovoltaic generation power prediction, and based on the wind power generation power prediction value of each wind power plant and the photovoltaic generation power prediction value of each photovoltaic power station, obtaining the total prediction power of the wind and light cluster power station. The energy storage capacity optimization configuration is carried out according to the actual load state and the total prediction power of the wind and light cluster power station, so that the accuracy of power prediction of the wind and light cluster power station is effectively improved, then the energy storage capacity optimization configuration is carried out according to the actual load state and the total prediction power of the wind and light cluster power station, and the configuration accuracy and reliability are improved. Therefore, the accuracy and reliability of energy storage capacity configuration are improved.

The invention discloses a slope safety early warning method in geotechnical engineering, and belongs to the technical field of geotechnical engineering, and the method comprises the following steps: obtaining geological conditions and reinforcement measures of a slope geotechnical engineering site, determining a slope model analysis area, and constructing a slope model; selecting a target monitoring point of the slope model; a slope safety early warning coefficient corresponding to slope instability is defined; slope characteristic parameters are obtained; constructing a back propagation neural network between the slope characteristic parameters and the rock-soil response, and performing Bayesian updating to obtain the rock-soil response of each excavation step; and constructing a relation mapping model of the rock-soil response and the slope safety early warning coefficient, obtaining the slope safety early warning coefficient of each excavation step, and performing slope safety early warning. According to the invention, the problem of insufficient early warning accuracy of the slope in geotechnical engineering is solved.

A variable curvature bending arc control method for a roll bending machine includes specific steps as follows: collecting and pre-processing data; initializing the network structure, and determining the parameters such as a number of input layer nodes, hidden layer nodes and output layer nodes and other parameters of the back propagation neural network according to the learning sample data; encoding a weight value and a threshold value of a back propagation neural network into individuals in a population according to set coding rules, and initializing the population according to a set population size and a random initialization method; GA-PSO iterative operation; initializing the back propagation neural network parameter; training the back propagation neural network; and an application of the control model. The control method accurately controls the variable curvature bending arc torque for the roll bending machine by combining genetic algorithm, back propagation neural network and particle swarm optimization.

The invention provides an atomic stamp-pad ink type identification method based on a hyperspectral imaging technology and a back-propagation neural network, which comprises the following steps: firstly, establishing a hyperspectral data database of atomic stamp-pad ink samples, and then extracting a region of interest of hyperspectral data of each stamp-pad ink sample to obtain a training set and a test set; then establishing a three-layer neural network based on a BP algorithm, and taking the spectral data of the atomic stamp-pad ink sample as nodes of an input layer; when atomic stamp-pad ink type identification needs to be achieved, hyperspectral data of a to-be-detected stamp is collected through a hyperspectral imaging system to serve as data of an input layer, and a result is output through matching. According to the method, a hyperspectral imaging technology is utilized to establish sample libraries of different atomic stamp-pad inks, and different neural network algorithms are compared, so that compared with traditional LSTM and RF algorithms, the BP algorithm is selected to accurately identify different types of atomic stamp-pad inks. The method disclosed by the invention can be used for quickly, accurately and nondestructively identifying the types of the atomic stamp-pad ink on the sample.

The invention discloses an agricultural machinery braking performance detector based on a Beidou satellite single-point positioning mode, and belongs to the technical field of braking performance detection. The agricultural machinery braking performance detector comprises a signal collector and a host, and the signal collector is internally provided with a satellite signal receiving module compatible with a BDS and a GPS; a sequence parameter extraction module, an integral distance measurement module, an error correction module, a slope calculation module and a performance evaluation module are arranged in the host; the method comprises the following steps: collecting three-dimensional motion data of a detected agricultural machine, calculating an uncorrected braking distance by using a complex Simpson integral algorithm, obtaining an error correction distance caused by single-point positioning signal drift based on a back propagation neural network, calculating the braking distance of the agricultural machine in combination with a slope correction value, and finally judging that the braking performance of the detected agricultural machine is qualified. According to the method, a ground differential base station and an external supporting assembly do not need to be erected, the problem that field detection deployment is tedious is solved, signal drifting and road slope interference are eliminated, and detection convenience and accuracy are improved.

The invention discloses a multi-input multi-output fuzzy neural network control method and system for a pseudo-ginseng planting greenhouse, and the method comprises the steps: obtaining original parameters including greenhouse temperature, humidity, illumination, water and fertilizer concentration, pseudo-ginseng growth state and water flow velocity, and carrying out the standardization of the original parameters, and obtaining input parameters; inputting the parameter into a three-layer back propagation neural network pre-training model, and outputting initial adjustment coefficients of six control quantities such as the opening degree of the sunshade; constructing a fuzzy rule base based on a parameter coupling scene, inputting a preliminary adjustment coefficient to obtain a fuzzy output quantity, and performing defuzzification through a gravity center method to obtain a corrected adjustment coefficient; a control instruction is calculated in combination with an equipment rated range, and closed-loop control is realized through dynamic adjustment. The system automatically operates through a multi-parameter acquisition module, an equipment control output module and the like. The application breaks through the traditional single-input single-output limitation, reduces the parameter fluctuation by more than 50%, adapts to the whole growth cycle of pseudo-ginseng, improves the utilization rate and yield of water and fertilizer, reduces the labor cost, and has remarkable economic benefits.

The invention provides a material constitutive parameter inversion method which can be applied to the technical field of artificial intelligence. The material constitutive parameter inversion method comprises the following steps: acquiring an elastic modulus and an actually measured load depth curve of a to-be-tested material in a single nanoindentation test; performing extrapolation translation preprocessing on the actually measured load depth curve to obtain an equivalent ideal curve; carrying out characterization processing on the equivalent ideal curve to extract input characteristic parameters which comprise press-in rigidity, unloading rigidity and plastic work proportion; the indentation rigidity, the unloading rigidity, the plastic work ratio and the elastic modulus are input into a pre-trained indentation constitutive mapping model to predict the yield strength and the strain hardening index of the to-be-tested material, and the indentation constitutive mapping model is a full-connection back-propagation neural network pre-trained through a finite element virtual test sample. The invention further provides a material constitutive parameter inversion system and device, a storage medium and a program product.

The invention relates to the technical field of heat treatment analysis or optimization, and particularly discloses a quenching process parameter optimization method, which comprises the following steps of: 1, acquiring material performance parameters of a to-be-optimized workpiece; 2, establishing a three-dimensional finite element model of a workpiece to be optimized; step 3, establishing an orthogonal experiment table for finite element simulation; 4, establishing a multi-objective optimization mathematical model and a weight coefficient function; step 5, constructing a back propagation neural network prediction model GA-BPNN optimized based on a genetic algorithm; and step 6, performing process parameter optimization by using a deep reinforcement learning Duelling DQN algorithm. The technical problem that in the prior art, a quenching process parameter optimization method does not consider the characteristic that importance of different workpieces to different performance parameters in different application scenes is different, so that the optimized quenching process parameters are disjointed with actual application is solved.

The invention discloses a wheat grain moisture content lossless prediction method based on hyperspectral imaging and Wasserstein generative adversarial network data enhancement, which realizes accurate moisture prediction by fusing spectral feature data enhancement and deep learning modeling. Collecting spectral image data of the wheat grains by using a visible light-near infrared and short wave infrared hyperspectral imaging system; a Wasserstein generative adversarial network is adopted to generate synthetic spectrum and moisture data in a differentiated mode, data distribution consistency is verified through t-SNE, and a triple training set is expanded; noise is eliminated in combination with first-order derivative preprocessing, key characteristic wavelengths are screened by using SPA and ReliefF algorithms, and a convolutional neural network regression model is constructed; experiments show that the method achieves # imgabs0 # imgabs1 # RMSEP = 0.5717 in the visible light-near infrared band and # imgabs2 # RMSEP = 1.0009 in the short wave infrared band, and the precision is remarkably improved compared with a traditional extreme learning machine and a back propagation neural network.

The invention provides a multi-source adaptive fusion positioning method and system and a medium, and belongs to the technical field of automatic driving and navigation positioning, and the method comprises the steps: receiving original data synchronously collected by each positioning module, extracting an input feature vector, and carrying out the standardization; inputting the standardized input feature vector into a pre-trained K-nearest neighbor model, and calculating to obtain an initial confidence coefficient corresponding to each positioning module; jointly inputting the initial confidence coefficient and the input feature vector into a back propagation neural network model to obtain a corrected sensor confidence coefficient vector, and generating a positioning mode recommendation classification; according to the positioning mode recommendation classification, adaptively selecting a working mode of the positioning system; based on an extended Kalman filter algorithm, the sensor confidence vector is used to perform state update, and parameters for vehicle navigation control are output. According to the method, GNSS false fixation is effectively detected, multi-source data are fused, the confidence coefficient is dynamically adjusted, and the positioning reliability is improved.

The invention discloses a borrowing type successive approximation analog-to-digital converter adopting a back propagation neural network for calibration. The borrowing type successive approximation analog-to-digital converter comprises a system composed of a positive and negative capacitance digital-to-analog converter adopting a three-stage split bridge type capacitor array, a bootstrap sampling switch, an automatic return-to-zero comparator, an SAR logic control unit, an original-to-binary module and a BPNN calibration engine. Bootstrap sampling switches are respectively arranged at the tail ends of the positive and negative capacitance digital-to-analog converters, a comparator is connected with the output ends of the two, and the output is connected with an SAR logic control unit; the original-to-binary module converts the code generated by the SAR logic control unit into a binary code; and a BPNN calibration engine receives the code and performs calibration calculation by using a trained back propagation neural network model. By implementing the converter disclosed by the invention, hardware implementation can be simplified while high performance is ensured, so that the ADC realizes efficient and accurate signal conversion under a 180-nanometer BCD process, and power consumption and cost are reduced.

The application relates to a method for predicting the amount of water flowing in front of a dam of the Three Gorges Reservoir based on an improved neural network model, which comprises the following steps: collecting the inflow and outflow data of the Three Gorges Reservoir and the amount of water flowing in front of the dam and dividing the data into training and test sets; setting the particle swarm algorithm, the improved neural network structure and parameters; training the improved neural network by using the training set, optimizing and solving the weight and threshold of the improved neural network by using the improved particle swarm algorithm during the training; calculating the prediction error and judging whether the error is qualified; and using the trained improved neural network for real-time prediction of the amount of water flowing in front of the dam of the Three Gorges Reservoir. After the training process of the error back propagation neural network is optimized and improved by using the particle swarm algorithm, the improved error back propagation neural network is used as the prediction model of the amount of water flowing in front of the dam of the Three Gorges Reservoir, the connection weight of the prediction model is closer to the optimal ideal value, the prediction precision of the amount of water flowing is improved, and the adaptability to the uncertainty of the data information of the reservoir dam is improved.

The invention discloses a weight function method for calculating a stress intensity factor of a circumferential weld surface crack of a pipeline. The method comprises the following steps: constructing a point load weight function for calculating the stress intensity factor of the circumferential weld surface crack of a pipeline structure; establishing a pipeline structure finite element model containing the circumferential weld surface cracks, and calculating a stress intensity factor reference solution under a reference load based on an M integral method; solving a weight coefficient of a calculation point of the front edge of the surface crack of the circumferential weld in the point load weight function, and optimizing a back propagation neural network through a genetic algorithm to establish a weight coefficient prediction model; and carrying out double integral operation on the product of the point load weight function and the stress distribution load on the circumferential weld surface crack surface to realize calculation of a stress intensity factor. The method solves the problems that an existing method is only suitable for the situation that the stress distribution load changes unidirectionally along the crack depth and is not suitable for stress distribution which changes bidirectionally along the crack depth and the crack length frequently occurring in an actual structure, the calculation cost is increased, and the calculation precision is reduced.

The application relates to the technical field of tunnel engineering disaster prediction, and discloses a soft rock tunnel large deformation prediction method based on a particle swarm optimization neural network, which comprises the following steps: selecting geological construction characteristic parameters such as water content of surrounding rock and construction methods as input factors, and establishing a qualitative index and quantitative value mapping; based on orthogonal test design and numerical simulation, engineering sample data sets are constructed and normalized; subsequently, the initial connection weight and bias of an error back propagation neural network are globally iteratively optimized by using a particle swarm optimization algorithm, so that optimal initial values are obtained by taking network errors as fitness values; finally, data training is carried out to generate a mature model, and a predicted deformation value is output and combined with a double check mechanism to carry out engineering early warning. The application solves the problems that traditional neural networks are slow in convergence and prone to falling into local optimization due to random parameter initialization, and effectively improves the prediction accuracy of soft rock tunnel large deformation and the reliability of engineering early warning.

The application discloses a runoff prediction method and device based on a Gaussian function and a medium, relates to the technical field of runoff prediction, and comprises the following steps: determining fitting values of a plurality of Gaussian parameters of each historical year based on monitored values of runoff of each month in each historical year; inputting the fitting values of each Gaussian parameter of all historical years into a Gaussian parameter prediction model of the corresponding Gaussian parameter respectively to obtain prediction values of each Gaussian parameter of a future year; determining Gaussian function values of each month in the future year based on the prediction values of each Gaussian parameter of the future year; and determining prediction values of runoff of each month in the future year based on the Gaussian function values of each month in the future year and a runoff prediction model. The runoff prediction model is obtained by training a back propagation neural network. The application improves the prediction accuracy of runoff.

The present invention discloses a network data transmission method and system for network virtual surgery, comprising: obtaining working online features of the network virtual surgery, inputting the working online features into a pre-trained back propagation neural network to obtain four monitoring output features; calculating a data compression rate, a prediction compensation window size, a number of redundant retransmissions, and a local interpolation smoothing factor based on the four monitoring output features; compressing the working data of the network virtual surgery according to the data compression rate and then performing data transmission; during the data transmission process of the network virtual surgery, compensating for delays based on a Kalman predictor of the prediction compensation window size, triggering redundant retransmissions according to the number of redundant retransmissions to combat packet loss, and substituting the local interpolation smoothing factor into an interpolation filter to smooth network jitter; and combining the back propagation neural network with a virtual surgery force feedback system to achieve adaptive adjustment of delay, jitter, and packet loss problems in the network virtual surgery.

The invention discloses a radar track target classification method based on statistical knowledge and a neural network, and the method comprises the steps: collecting original track data, and carrying out the marking and cleaning processing, and obtaining a standard track data set; extracting kinematics, bistatic radar cross section and track morphological features based on the standard track data set, and constructing an original feature set; performing feature weight learning through a neighborhood component analysis method, and screening according to a preset weight threshold to obtain an effective feature subset; performing statistical distribution analysis on the effective feature subsets according to target categories, determining significant features of each target category, and setting a threshold interval; performing preliminary classification based on a statistical knowledge classifier, and determining whether the to-be-classified track is an easily-classified sample or a difficultly-classified sample through a category discrimination function; and classifying the samples difficult to classify based on a back propagation neural network classifier, outputting the probability value of each target category through a neural network model, and taking the target category with the maximum probability value as a final classification result. According to the invention, the target classification accuracy and real-time performance can be improved.

The application discloses a stiffness and damping optimization method, system, device, medium and product of a vehicle suspension based on a back propagation neural network, and relates to the technical field of vehicles. The method comprises the following steps: training a back propagation neural network based on a training set to obtain a trained back propagation neural network; extracting the trained back propagation neural network by adopting a Taylor expansion method and a finite difference method to obtain a matrix; constructing a nonlinear multivariate inter-axle stiffness-damping objective function based on the matrix; obtaining an input variable sequence and an output variable sequence of a vehicle; and optimizing the stiffness and damping of the vehicle suspension under a constraint condition by taking the function value of the nonlinear multivariate inter-axle stiffness-damping objective function as the target, so as to obtain an optimal stiffness-damping matrix and complete the stiffness and damping optimization of the vehicle suspension. The application can improve the driving smoothness and steering stability of the vehicle.