262 results about "Backpropagation neural nets" patented technology

The invention belongs to the technical field of power distribution network voltage regulation and control, and discloses a distributed photovoltaic complex power prediction-cluster-cooperation-based power distribution network voltage regulation and control method, which integrates photovoltaic historical data, inputs an improved back propagation neural network model and outputs predicted photovoltaic active power output. Estimating the reactive capacity boundary of each node in real time based on the running state of the network-following inverter; dividing a distributed photovoltaic cluster by establishing a two-dimensional modularity function of a net load index and an equivalent electrical distance; a multi-device differential cooperative control strategy is provided for the voltage out-of-limit risk in the cluster; and constructing an optimization function with minimum network loss and voltage offset as a target, and optimizing and solving the function by using an improved multi-organization particle swarm optimization algorithm to obtain a multi-device adjustment sequence and a device action amount. According to the method, the renewable energy consumption capacity is improved and the network loss is reduced while the voltage stability of the power distribution network is ensured, and the comprehensive adjustment cost is optimized.

The invention relates to the technical field of battery protection control, and discloses a battery charging protection method and system based on intelligent control and a storage medium. The method comprises the following steps: acquiring and calculating battery parameters to obtain a state value; constructing a neural network model based on the state value to output a charging parameter; generating a charging strategy according to the charging parameters; the charging strategy is input into the control circuit for execution; executing multi-stage safety protection to obtain a result; and updating neural network model optimization control parameters according to a result. According to the invention, the charging control model based on the back-propagation neural network is constructed, and the real-time battery state monitoring, the self-adaptive charging strategy generation, the multi-stage safety protection mechanism and the self-learning optimization function are combined, so that the precise control and the comprehensive protection of the battery charging process are realized, the service life of the battery is effectively prolonged, and the charging safety is improved.

The invention relates to the technical field of aging detection, and discloses a xenon lamp simulation environment aging detection method and system for a water-based waterproof material. The method comprises the following steps: testing a water-based waterproof material sample in an aging detection platform and collecting first aging response data; calculating a characteristic spectrum drift parameter combination in combination with the first aging response data, and solving a multi-factor aging dynamics equation set to obtain three-dimensional environment field data; inputting the three-dimensional environment field data into a back propagation neural network to carry out multi-factor environment parameter collaborative optimization to obtain an intelligent control parameter combination; performing test parameter adjustment and detection on the aging detection platform according to the intelligent control parameter combination to obtain second aging response data; and performing spectral attenuation index calculation and aging degree grading based on the second aging response data to obtain an aging state evaluation result of the water-based waterproof material sample. According to the method, multi-dimensional quantitative characterization of the aging state of the water-based waterproof material and scientific prediction of the service life of the water-based waterproof material in an acid pollution environment are realized.

The embodiment of the invention provides a lithium battery health comprehensive evaluation method and system, electronic equipment and a storage medium, and belongs to the technical field of lithium batteries. According to the scheme, battery characteristic data of a lithium battery are obtained, and the battery characteristic data comprise effective capacity, discharge depth, temperature variation, charging time, current variation and voltage variation; feature extraction is carried out on the battery feature data to obtain a candidate feature set, and the candidate feature set comprises a plurality of candidate features; performing correlation analysis on the candidate feature set to obtain health features; on the basis of the health features, a comprehensive evaluation model is used for battery health state estimation and remaining service life prediction, a comprehensive evaluation result is obtained, the comprehensive evaluation model is obtained by training a back propagation neural network through a particle swarm optimization, and the scheme can improve the accuracy and robustness of the lithium battery health evaluation result.

The invention belongs to the technical field of microwave remote sensing, and provides an earth surface soil humidity GNSS-R downscaling inversion method based on multi-model fusion. Comprising the following steps: firstly, based on CYGNSS observation data, SMAP SM and auxiliary element data, constructing a training data set of a 36km grid and a test data set of a 3km grid through quality control; a limit gradient enhancement algorithm, a lightweight gradient elevator, a back propagation neural network and a random forest are adopted as base models, the training data set is divided randomly according to the proportion of 7: 3, and all the base models are constructed for training and verification; and finally, deeply learning a test result of each base model through a meta-learner (linear regression model), and automatically adjusting the weight of the test result to obtain a multi-model fused soil humidity downscaling inversion result. According to the method, the advantages of a plurality of base models are fully fused, and the soil humidity downscaling inversion precision can be effectively 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 discloses a method for optimizing plant extract preparation based on machine learning and a multi-objective algorithm, and belongs to the technical field of plant extraction. The method comprises the steps that firstly, data in the plant extract preparation process are collected, then a non-linear mapping relation between process parameters and product quality is established through a back propagation neural network model, prediction of multiple quality indexes of an extraction process and a product is achieved, and finally the process parameters are optimized in combination with a multi-objective optimization algorithm. The optimized process parameters are adopted to prepare the plant extract, so that the nutrition and sensory quality of the extracted product can be remarkably improved. The method meets multi-target, complex and high-dimensional optimization scenes, is suitable for preparation of any plant extract, and is wide in application range.

The invention relates to the technical field of intelligent welding and welding processes, in particular to a double-wire welding process parameter prediction method based on a machine learning method. The method comprises the following steps that welding parameters and weld joint morphology parameter data are obtained through a welding process test, the data are preprocessed, and a data set is established; constructing a back propagation neural network model based on the data set, constructing a support vector machine model, and training the back propagation neural network model and the support vector machine model by using the data set; evaluating the trained back propagation neural network model and the support vector machine model, and selecting an optimal model as a model for actual industrial application; and inputting parameters required by a weld bead to be welded into the selected model to obtain double-wire welding process parameters. According to the design, the welding process parameters are predicted through the method based on machine learning, so that the quality stability and efficiency of double-wire welding are improved.

The invention relates to a silicon photonic interlayer coupling structure design method based on an artificial neural network, and belongs to the technical field of artificial intelligence. The method comprises the following steps: acquiring a plurality of interlayer coupling structure data sets, performing normalization processing on the interlayer coupling structure data sets, and dividing the data sets; constructing a back propagation neural network; in the design process, a self-adaptive genetic algorithm is used for optimizing the weight and the threshold value of the back-propagation neural network; using a training set to train the back propagation neural network model; coupling structure parameters are input into the trained model, a coupling efficiency value is output, and whether the coupling structure meets design requirements or not is judged according to the coupling efficiency value; and continuously adjusting the structural parameters until the design requirements are met. According to the method, the design time of the interlayer coupling structure is shortened, the design difficulty of the interlayer coupling structure is reduced, and automation and intelligence of the design of the interlayer coupling structure are realized.

The invention relates to the technical field of grass carp nondestructive testing, in particular to a grass carp muscle unsaturated fatty acid content nondestructive testing method based on scale characteristic wavelength. The method comprises the following steps: constructing a back propagation neural network, and training the network by using a plurality of samples containing scale wavelength information and unsaturated fatty acid information to obtain a prediction model; and the fresh grass carp scale wavelength information is input into the prediction model to obtain the unsaturated fatty acid content of the living grass carp muscle, so that rapid nondestructive detection can be realized, and the detection accuracy is relatively high.

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 discloses a network data transmission method and system for a network virtual surgery, and the method comprises the steps: obtaining a work online feature of the network virtual surgery, and inputting the work online feature into a pre-trained back-propagation neural network to obtain four monitoring output features; calculating to obtain a data compression rate, a prediction compensation window size, redundancy retransmission times and a local interpolation smoothing factor according to the four monitoring output characteristics; data transmission is executed after working data of the network virtual surgery is compressed according to the data compression rate; in the data transmission process of the network virtual surgery, delay is compensated based on a Kalman predictor predicting the size of a compensation window, redundant retransmission is triggered according to the number of redundant retransmission times to resist packet loss, and a local interpolation smoothing factor is substituted into an interpolation filter to smooth network jitter; a back propagation neural network is combined with a virtual operation force feedback system, and adaptive adjustment of delay, jitter and packet loss problems in a networked virtual operation is realized.

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 discloses a water supply network leakage detection method based on time-frequency domain characteristics of a ground penetrating radar, and relates to the technical field of leakage detection. The method comprises the steps that echo signals, collected by the ground penetrating radar, of different areas of the water supply pipe network and leakage detection results of all the areas are obtained; the leakage detection results comprise non-leakage, leakage and leakage positions; extracting time-frequency domain characteristics of the echo signals; performing feature screening in the time-frequency domain features by adopting a random forest algorithm and a principal component analysis method to obtain effective features of the echo signals; forming a training data set by the effective features of all the echo signals and the leakage detection results of different areas; training a back propagation neural network by adopting the training data set; and inputting effective characteristics of echo signals of the water supply network to be detected, which are acquired in real time, into the trained back propagation neural network, and outputting a leakage detection result of the water supply network to be detected. The accuracy and efficiency of leakage detection of the water supply network can be improved.

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 process parameter optimization method for an entropy alloy coating in high-speed laser cladding, and belongs to the field of high-speed laser cladding. Comprising the following steps that firstly, laser cladding process parameters serve as input, the aspect ratio and microhardness of a cladding coating serve as output, and a high-speed laser single-channel cladding experiment is conducted through a center composite design (CCD) experiment scheme; then, according to a CCD experiment result, establishing a back propagation (GABP) neural network prediction model optimized by a genetic algorithm based on laser cladding process parameters and cladding layer aspect ratio and microhardness; and finally, on the basis of a second-generation non-dominated sorting genetic algorithm (NSGA-II), by taking the aspect ratio of the cladding layer and the microhardness as optimization targets and taking a GABP neural network prediction model as a fitness function of the NSGA-II, obtaining a Pareto leading-edge solution set through global optimization to complete process parameter optimization. The method has the technical effects that the defects of a trial and error method, a single variable method and other traditional optimization methods can be avoided, the experiment time is saved, the cost is saved, the cladding layer which is metallurgically bonded with the matrix and has no obvious cracks and holes can be obtained through the determined optimal process parameter combination, and the method is simple, high in universality and suitable for industrial production. And the method can be popularized and used for determining process parameters of other coatings in high-speed laser cladding.

The invention relates to the technical field of electric digital data processing, in particular to a single point mooring system liquid slip ring spring energy storage sealing ring optimization design method which comprises the following steps: establishing a two-dimensional axial symmetry model of a liquid slip ring spring energy storage sealing ring; the peak contact stress, the linear contact stress and the maximum shear stress of the liquid slip ring spring energy storage sealing ring under the actual working condition are obtained; establishing a sealing performance evaluation system of the liquid slip ring spring energy storage sealing ring; screening out key structure parameters to form a data set; constructing a back-propagation neural network model, training the weight and bias of the back-propagation neural network model, evaluating the trained back-propagation neural network model, and finally obtaining an optimal parameter combination and a finite element model based on the optimal parameter combination. According to the method, the optimal parameter combination meeting the sealing performance requirement can be determined, so that the overall sealing effect is improved, and the sealing reliability 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 invention discloses a mechanical arm error compensation method based on a quantum particle swarm optimization BP neural network, which is mainly reflected in a high-dimensional, nonlinear, multi-peak and dynamic complex environment, can adaptively adjust a compensation strategy in real time, and realizes accurate compensation of geometric-non-geometric coupling errors. The method adopts a back propagation neural network (BPNN) to predict the joint angle error of the robot, can adaptively learn and approach nonlinear error features, solves the problem of poor precision compensation caused by numerous parameters and difficult decoupling of an exponential product (POE) model, optimizes the parameters of the BP neural network through a quantum particle swarm optimization (QPSO), and improves the precision of the robot. The global search capability is enhanced, the local optimal solution is avoided, the convergence speed is improved, the training efficiency of the BP neural network is improved, and finally the compensation precision and the system stability are remarkably improved.

The invention discloses a runoff prediction method and device based on a Gaussian function and a medium, and relates to the technical field of runoff prediction, and the method comprises the steps: determining the fitting values of a plurality of Gaussian parameters of each historical year based on the monitoring value of the runoff of each month in each historical year; respectively inputting the fitted values of the Gaussian parameters in all historical years into a Gaussian parameter prediction model of the corresponding Gaussian parameters to obtain predicted values of the Gaussian parameters in the future year; determining a Gaussian function value of each month in the future year based on the predicted value of each Gaussian parameter in the future year; determining a predicted value of the runoff volume of each month in the future year based on the Gaussian function value of each month in the future year and a runoff volume prediction model; the runoff prediction model is obtained by training a back propagation neural network. According to the invention, the prediction precision of the runoff volume is improved.

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 power distribution terminal defect detection method and device and a computer program product, and the method comprises the steps: building a power distribution terminal operation model based on a dynamic simulation platform, analyzing and determining defect factors affecting the operation state of a power distribution terminal, and building a power distribution terminal defect type classification system; optimizing a VAE model structure of the variational auto-encoder, replacing an original back propagation BP neural network in the VAE model with a GRU network, and constructing a GRU-VAE hybrid network model; and on the basis of the GRU-VAE hybrid network model, feature extraction and anomaly detection are carried out on operation state data of the power distribution terminal equipment, a system fault state is identified through a preset fault determination threshold, and detected defects are classified according to the defect type classification system. According to the method, multiple defect types can be identified, the operation and maintenance requirements of a large-scale power distribution terminal can be met, powerful technical support is provided for stable operation of the power distribution Internet of Things, and the method has high practicability and popularization value.

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 provides a complex rock mass stability assessment method based on a back propagation neural network, and aims to provide an efficient and accurate assessment means for rock mass stability analysis under complex geological conditions. According to the method, the rock mass numerical model is constructed, influence factors are determined, and the stability of the rock mass is quantitatively analyzed and evaluated by using the back propagation neural network, so that the multi-scale characteristics of the rock mass, the influence of an interlayer staggered belt and external loading conditions can be comprehensively considered, and the stability of the rock mass in different stress states can be accurately predicted; scientific basis is provided for rock engineering design and construction, and engineering risks are reduced.

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.

The invention provides a power system intelligent fault diagnosis method and system based on data fusion, and relates to the technical field of intelligent monitoring. The method comprises the following steps: acquiring vibration signals and electric signals of rotating equipment of different fault types in real time by using a sensor, and correspondingly obtaining a vibration matrix and an electric matrix; respectively carrying out weighted combination on the vibration matrix and the electric matrix to obtain a vibration element confidence coefficient and an electric element confidence coefficient; performing normalization processing and fusion processing on the vibration element confidence coefficient and the electric element confidence coefficient to obtain a fusion matrix; performing dimension reduction processing on the fusion matrix by using a principal component analysis method to obtain fusion features; and inputting the fusion features into a back propagation neural network for training to obtain a diagnosis model used for diagnosing the fault type of the rotating equipment. According to the invention, the problems of single characteristic parameter, low mechanical fault diagnosis identifiability and large error of the fault diagnosis method of the rotating equipment are 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.