34results about How to "Good nonlinear approximation capability" patented technology

The invention discloses a multivariable logistics freight volume prediction method based on an LSTM network, which is used for solving the technical problem of low prediction precision in time seriesdata prediction in the prior art. The method comprises the following steps of: screening logistics freight volume influence factors and preprocessing influence factor data; converting a time series data set supervised learning mode; normalizing the time series data variables of the supervised learning format; dividing a data training set and a test set; setting parameters of an LSTM prediction model and carrying out forward training on the model; and performing back propagation of the model and back normalization of a logistics freight volume prediction value. According to the method, the long-term memorability of the LSTM network for the flow data is fully utilized, the relation between variables can be effectively explored through supervised learning, and the logistics freight volume prediction precision is improved.

The invention relates to a CAN bus-based pig house environmental temperature intelligent monitoring system. The system is characterized in that the intelligent monitoring system is composed of a CAN bus-based pig house environment parameter acquisition and intelligent prediction platform, a pig house environment multi-point temperature fusion model and a pig house environment intelligent prediction model. With the CAN bus-based pig house environmental temperature intelligent monitoring system of the present invention adopted, an existing pig house monitoring system cannot intelligently monitor and predict the temperature of the environment of a pig house according to the non-linearity and large lag of the environmental temperature change of the pig house, the large area of the pig house, complicated temperature change and other characteristics, as a result, control and adjustment of the environmental temperature of the pig house are seriously affected, while, with the CAN bus-based pig house environmental temperature intelligent monitoring system adopted, the above problem can be solved.

The invention relates to a segment neural network friction model based dual-motor servo system control method and belongs to the technical field of electromechanical control. The method includes: firstly, analyzing a dual-motor drive servo system with friction, and establishing a mathematical model of the dual-motor drive servo system with friction according to a mechanism modelling method as well as the structures of motors and the physical law; secondly, analyzing a friction term fi in the mode, and utilizing a segment neural network to establish a friction model of the nonlinear friction fi so as to obtain a segment neural network friction model; thirdly, utilizing a terminal sliding mode control algorithm to acquire a synchronous motor speed control law, and performing synchronous tracking control on the dual-motor servo system according to the control law. By the method, influence on the dual-motor system due to friction can be eliminated, the dual-motor system has better transient performance, tracking response speed of the dual-motor servo system is increased effectively, and fast synchronization of the dual-motor system can be guaranteed.

The invention discloses an electric vehicle power battery SOC intelligent detection device which is characterized in that the intelligent detection device includes a battery parameter acquisition platform and a battery SOC estimation system, the battery parameter acquisition platform collects real-time parameters of voltage, current and temperature of an electric vehicle power battery group, the battery SOC estimation system estimates an SOC value through the collected parameters, and a battery SOC system is a nonlinear, time-delay and multivariable coupling complex real-time system with a high requirement. According to the device, a problem that a conventional detection device can not obtain an ideal effect of the intelligent detection of the electric vehicle power battery SOC.

The invention discloses an electric automobile power battery SOC (State of Charge) detection system. The characteristics lie in that the detection system comprises a battery parameter acquisition platform and a battery SOC estimation system, the battery parameter acquisition platform is responsible for real-time parameter acquisition for the voltage, current and temperature of an automobile power battery pack, and the battery SOC estimation system can accurately estimate a battery SOC value through the acquired parameters; and the battery SOC is a non-linear, delayed, multivariable coupling and complex highly demanding real-time system. The detection system effectively solves a problem that the traditional automobile battery SOC estimation method is difficult to achieve an ideal effect.

The invention discloses an intelligent tomato greenhouse temperature early-warning system based on a minimum vector machine. The early-warning system is characterized by being composed of a tomato greenhouse environmental parameter acquisition and intelligent prediction platform based on a CAN field bus and an intelligent tomato greenhouse temperature early-warning system. By means of the intelligent tomato greenhouse temperature early-warning system based on the minimum vector machine in the invention, many problems still in the environment in a closed tomato greenhouse due to the reasons ofunreasonable design, backward equipment, incomplete control system and the like in the traditional tomato greenhouse environment can be effectively solved; and furthermore, the control problem that the tomato greenhouse environment temperature is greatly influenced due to the fact that the existing tomato greenhouse environment monitoring system does not monitor and predict the temperature in thetomato greenhouse environment according to the characteristics of nonlinearity and large lag of tomato greenhouse environmental temperature change, large tomato greenhouse area, complex temperature change and the like can be effectively solved.

The invention discloses an intelligent SOC (State of Charge) prediction device for an electric vehicle power battery, which is characterized by comprising a battery parameter acquisition platform and a battery SOC prediction system, wherein the battery parameter acquisition platform is used to acquire real-time parameters of voltage, current, temperature, and ambient temperature of the vehicle power battery pack; and the battery SOC prediction system is used to predict the battery SOC value through the acquired real-time parameters. The battery SOC is a nonlinear, delayed, multivariable-coupling, and complex real-time system with extremely high real-time performance requirements. The problem that the conventional prediction device can not achieve ideal battery SOC prediction precision effects can be effectively solved.

The invention discloses a method for fuzzy identification of a water supply network model based on waterpower adjustment. According to the invention, the EPANETH software is adopted to establish a waterpower adjustment model of the water supply network, the operating simulation to the water supply network is realized, and the simulated experimental data is obtained. The system input and output time history obtained through simulating the waterpower adjustment model is utilized to replace the observation data, and the fuzzy identification method of the T-S model is adopted to deal with the input and output time history, so that a system parameter model is obtained. According to the invention, the advantages of the EPANETH software are utilized, stationing and data collection of onsite sensors are simplified, and the high-precision fuzzy identification of the T-S model is utilized for predictive control of the water supply network, so that the reliability of optimized dispatching of the water supply network is ensured.

The present invention discloses a tomato greenhouse environmental parameter intelligent monitoring device based on an ANFIS neural network. The intelligent monitoring device is composed of a wirelesssensor network-based tomato greenhouse environmental parameter intelligent detection platform and a tomato greenhouse yield intelligent early warning system. According to the tomato greenhouse environmental parameter monitoring and regulation platform established by the present invention, the problem that the tomato greenhouse yield cannot be predicted and early warned according to the impact of tomato soil moisture on the tomato greenhouse yield in the prior art is effectively solved.

The invention elates to the technical field of industrial process control, and discloses a method and system applicable to complementing missing data of product quality indicators in the complex industrial process based on selective double-layer ensemble learning. The method comprises the steps of firstly extracting different dimensions of variables of the sampled data to generate multiple sampling sets which serve as training sets of sub-models; then modeling each sub-model by respectively adopting a vector machine method, a BP neural network method and a partial least squares method; and finally putting forward a complementing effect evaluation indicator to perform evaluation on the complementing effect of each sub-model, and selecting a plurality of sub-models with the best complementing effect to perform selective ensemble. The method makes full use of all variables of the training samples, has a good data complementing effect, and facilitates enterprises to obtain the actual operation condition of the production process according to the analysis so as to perform targeted production operation optimization.

The invention provides a nonlinear image multi-scale geometric representation method. The technical scheme comprises the following steps of 1, filtering an image by using an FIR medium value mixed filter; 2, performing 2-reducing sampling on the filtered image according to an interlacing inter-row mode to obtain a lower sampling image; step 3, performing medium value-based 2-increasing interpolation filtering on the lower sampling image to obtain an interpolation estimating image; step 4, subtracting the original image by the interpolation estimating image to obtain multiple scale layer nonlinear pyramid decomposing images; step 5, performing Shear directional filtering on each scale layer nonlinear pyramid decomposing image to obtain a sub-band image. The nonlinear image multi-scale geometric representation method belongs to an image nonlinear multi-scale geometric representation method with excellent performance, is less in operand, and has higher application values in aspects such as image compressing, edge extracting, grain retrieving.

The invention discloses a degradation prediction method based on a quantum attention cycle coding and decoding neural network (QAREDNN). The QAREDNN is used in the method, a quantum attention mechanism is introduced to reconstruct an encoder and a decoder at the same time, so that the QAREDNN can fully mine and pay attention to important information, interference of redundant information is inhibited, and better nonlinear approximation capability is obtained. A quantum threshold cycle unit (QGRU) of which the active value and the weight are replaced by a quantum rotation matrix is constructedby adopting quantum neurons to replace traditional cycle units in an encoder and a decoder, so that the generalization ability and the response speed of the QAREDNN can be improved; in the training process of QAREDNN, an LM algorithm is introduced to achieve rapid updating of the rotation angle and attention parameters of a quantum rotation matrix. Due to the advantages of the QAREDNN in the aspects of nonlinear approximation capability, generalization capability, response, training speed and the like, the degradation prediction method based on the quantum attention cycle coding and decoding neural network can obtain higher prediction precision and calculation efficiency.

The invention discloses a cucumber greenhouse temperature intelligent detection device based on an LVQ neural network. The cucumber greenhouse temperature intelligent detection device is characterizedby comprising a cucumber greenhouse environment parameter acquisition platform based on a CAN bus and a cucumber greenhouse temperature intelligent monitoring system. The device effectively solves the problem that the existing cucumber greenhouse monitoring system does not intelligently monitor and predict the temperature of the cucumber greenhouse environment according to the characteristics ofnonlinearity and large lag of the temperature change of the cucumber greenhouse environment, large area and complex temperature change of the cucumber greenhouse and the like, so that the regulation and control of the temperature of the cucumber greenhouse environment are greatly influenced.

The invention discloses a space rolling bearing residual life prediction method based on VETMRRN, and the method comprises the following steps: S1, extracting time domain and frequency domain features from the original vibration acceleration data of a space rolling bearing, carrying out the shape value feature fusion, and taking the time domain and frequency domain features as the performance degradation features of the space rolling bearing; s2, inputting the performance degradation characteristics of the space rolling bearing into the VETMRRN to train hyper-parameters and network parameters of the VETMRRN; s3, utilizing VETMRRN to predict the performance degradation characteristic trend of the space rolling bearing in multiple steps; and S4, establishing a Weibull distribution reliability model, and predicting the precision failure threshold time point and the residual life of the space rolling bearing. According to the space rolling bearing residual life prediction method based on the VETMRRN, the VETMRRN is constructed and has good nonlinear approximation capability, generalization performance and calculation efficiency, so that the space rolling bearing residual life prediction method based on the VETMRRN has high prediction precision, good generalization performance and high calculation efficiency.

The invention discloses an electric automobile power battery SOC (State of Charge) detection system. The characteristics lie in that the detection system comprises a battery parameter acquisition platform and a battery SOC estimation system, the battery parameter acquisition platform is responsible for real-time parameter acquisition for the voltage, current and temperature of an automobile power battery pack, and the battery SOC estimation system can accurately estimate a battery SOC value through the acquired parameters; and the battery SOC is a non-linear, delayed, multivariable coupling and complex highly demanding real-time system. The detection system effectively solves a problem that the traditional automobile battery SOC estimation method is difficult to achieve an ideal effect.

The invention discloses a curve envelope fitting method based on a VGG16 network, and the method comprises the following steps: training a neural network by using a data set sample which is provided with a label and is acquired and established by a CCD, and applying the neural network algorithm to an acquired data set to verify the accuracy and calculate the microscopic morphological characteristics of the surface of an optical fiber; creating a read data set through a tenserflow framework, recording a gray value change sequence of the group of images at a certain pixel point (a, b) as X(a, b)(t), supplementing the sequence X(a, b)(t) into a one-dimensional sequence X2(a, b)(t) with the size of 224 * 224 by adopting a cubic Hermite interpolation method, and then converting the sequence X2(a, b)(t) into a two-dimensional image matrix X2(a, b)(m, n); processing and outputting the predicted actual height of the pixel point through a specially designed convolutional neural network, and comparing the actual height of the pixel point with a sample label to enable an error to be within a set threshold range. The application of the neural network enables the algorithm to have better self-learning, self-organizing and fault-tolerant capabilities and excellent nonlinear approximation capability, can improve the accuracy and fault-tolerant capability of the envelope algorithm, and has certain reference significance.

The invention elates to the technical field of industrial process control, and discloses a method and system applicable to complementing missing data of product quality indicators in the complex industrial process based on selective double-layer ensemble learning. The method comprises the steps of firstly extracting different dimensions of variables of the sampled data to generate multiple sampling sets which serve as training sets of sub-models; then modeling each sub-model by respectively adopting a vector machine method, a BP neural network method and a partial least squares method; and finally putting forward a complementing effect evaluation indicator to perform evaluation on the complementing effect of each sub-model, and selecting a plurality of sub-models with the best complementing effect to perform selective ensemble. The method makes full use of all variables of the training samples, has a good data complementing effect, and facilitates enterprises to obtain the actual operation condition of the production process according to the analysis so as to perform targeted production operation optimization.

The invention relates to a trend prediction method based on double-hidden-layer quantum circuit cyclic unit neural network, comprising the following steps: constructing a permutation entropy set of original operating data; inputting the permutation entropy set into DHL-QCRUNN for training and prediction, and obtaining a predicted permutation entropy set ;Construct the permutation entropy error set of the predicted value and the actual value at each time point; input the permutation entropy error set into DHL-QCRUNN training and prediction, and obtain the predicted normalized permutation entropy error set; denormalize the processing to obtain the final prediction result. The present invention proposes a new type of quantum neural network—a double-hidden layer quantum circuit recurrent unit neural network. The present invention uses the LM algorithm to update the network parameters of DHL-QCRUNN to improve the convergence performance of the neural network, which is comparable to other artificial intelligence methods. Compared with DHL-QCRUNN, DHL-QCRUNN has better nonlinear approximation ability, generalization characteristics and faster convergence speed. The present invention is used to predict the running trend of the monitored object, and achieves higher prediction accuracy, prediction stability and calculation efficiency.

The invention relates to a dual-motor servo system control method based on a segmented neural network friction model, and belongs to the technical field of electromechanical control. Firstly, the double-motor driven servo system with friction is analyzed, and the mathematical model of the double-motor driven servo system with friction is established according to the mechanism modeling method, according to the structure and physical laws of the motor. Then the friction term fi in the model is analyzed, and the friction model of friction nonlinear fi is established by using the segmental neural network. The obtained segmental neural network friction model uses the terminal sliding mode control algorithm to obtain the motor speed synchronous control law, and completes the synchronous tracking control of the dual-motor servo system according to the control law. The method of the invention can eliminate the influence of friction on the dual-motor system, enable the dual-motor system to have better transient performance, effectively improve the tracking response speed of the dual-motor servo system, and ensure the rapid synchronization of the dual-motor system.

The invention discloses an intelligent forecasting device for cucumber greenhouse output based on a recursive neural network. Networked cucumber greenhouse parameter detection platform and greenhouse cucumber output intelligent prediction system; the purpose of the invention is to provide a cucumber greenhouse output intelligent prediction device based on recursive neural network, and the intelligent early warning system detects the environmental parameters, soil parameters and parameters of cucumber greenhouse in real time. Cucumber greenhouse production information, so as to improve the production management of cucumber greenhouse and improve economic benefits.

The invention provides a nonlinear image multi-scale geometric representation method. The technical scheme comprises the following steps of 1, filtering an image by using an FIR medium value mixed filter; 2, performing 2-reducing sampling on the filtered image according to an interlacing inter-row mode to obtain a lower sampling image; step 3, performing medium value-based 2-increasing interpolation filtering on the lower sampling image to obtain an interpolation estimating image; step 4, subtracting the original image by the interpolation estimating image to obtain multiple scale layer nonlinear pyramid decomposing images; step 5, performing Shear directional filtering on each scale layer nonlinear pyramid decomposing image to obtain a sub-band image. The nonlinear image multi-scale geometric representation method belongs to an image nonlinear multi-scale geometric representation method with excellent performance, is less in operand, and has higher application values in aspects such as image compressing, edge extracting, grain retrieving.

The invention discloses an intelligent monitoring system of multiple oil and gas concentration sensors based on the Internet of Things. The system is composed of a ZigBee network-based gas station oil tank area environmental parameter acquisition platform and a gas station oil tank area environmental oil gas concentration sensor monitoring subsystem. The present invention effectively solves the problem that the existing gas station oil tank area environmental monitoring system does not accurately monitor the gas station oil tank area environmental oil gas concentration according to the characteristics of non-linearity, large hysteresis and complex changes in the gas station oil tank area environmental oil and gas concentration. Detection and early warning of sensor failures, thereby improving the accuracy and robustness of predicting oil and gas concentrations in gas stations.

Provided is an intelligent prediction system for a power battery SOC of an electric vehicle, the intelligent prediction system comprising a battery parameter collection platform and a battery SOC prediction system, wherein the battery parameter collection platform is used for collecting real-time parameters of the voltage, current and temperature of a vehicle power battery pack and an ambient temperature; and the battery SOC prediction system predicts, through the collected real-time parameters, a battery SOC value. The battery SOC is a real-time system which is non-linear, time-delayed, multi-variable coupled and complex, with high demands on real time performance. The intelligent prediction system effectively solves the problem that it is difficult for a conventional prediction device to obtain an ideal effect of battery SOC prediction accuracy.

The invention discloses a location-free detection method used when an SR motor travels to a region with phase electrical inductance changeless with angle variation, which comprises the following steps of: 1, determining the comprehensive performance parameter of an SR motor to be detected and selecting a conventional location-free detection method suitable for the SR motor to be detected; 2, establishing an actual mathematical model of the SR motor to be detected; and 3, carrying out zoning angular location detection on the SR motor to be detected by utilizing a processor: firstly demarcating the region with the phase electrical inductance changeless with the angle variation, and applying the original location-free detection method for location estimation before a conduction phase of the motor enters the region; and when the conduction phase of the motor enters the region with the phase electrical inductance changeless with rotor variation, resetting a constant speed detection region range for realizing the detection of the angle calculated by speed. The invention has simple steps, convenient implementation, strong practicality, high detection precision and small error, and can effectively solve the defects and insufficiency of low detection precision, huge error and the like existing in traditional method used when the SR motor travels to the region with the phase electrical inductance changeless with angle variation.

The invention relates to a CAN bus-based pig house environmental temperature intelligent monitoring system. The system is characterized in that the intelligent monitoring system is composed of a CAN bus-based pig house environment parameter acquisition and intelligent prediction platform, a pig house environment multi-point temperature fusion model and a pig house environment intelligent prediction model. With the CAN bus-based pig house environmental temperature intelligent monitoring system of the present invention adopted, an existing pig house monitoring system cannot intelligently monitor and predict the temperature of the environment of a pig house according to the non-linearity and large lag of the environmental temperature change of the pig house, the large area of the pig house, complicated temperature change and other characteristics, as a result, control and adjustment of the environmental temperature of the pig house are seriously affected, while, with the CAN bus-based pig house environmental temperature intelligent monitoring system adopted, the above problem can be solved.

The invention discloses a multi-objective optimization design method for a magnetic suspension flywheel motor based on a kriging approximation model, and the method employs the current stiffness and displacement stiffness of the magnetic suspension flywheel motor as optimization objectives, and optimizes the number of turns of suspension winding coils, the width of suspension teeth, the height of rotor teeth, and the axial length of the motor. Therefore, the suspension supporting rigidity of the flywheel battery under the vehicle-mounted complex working condition is effectively improved. Besides, according to the optimization design method provided by the invention, a finite element model of the motor is replaced by a Kriging approximation model, so that the calculation cost in the optimization iterative calculation process of the motor is reduced, and the optimization efficiency is improved; an improved multi-target fruit fly algorithm is adopted to optimize, a search space and a taste judgment value are improved in an original fruit fly algorithm, a fast non-dominated sorting and crowding distance sorting method is introduced to solve the multi-target optimization problem, and the global search ability and convergence speed of the algorithm are effectively improved.

The invention discloses an intelligent tomato greenhouse temperature early-warning system based on a minimum vector machine. The early-warning system is characterized by being composed of a tomato greenhouse environmental parameter acquisition and intelligent prediction platform based on a CAN field bus and an intelligent tomato greenhouse temperature early-warning system. By means of the intelligent tomato greenhouse temperature early-warning system based on the minimum vector machine in the invention, many problems still in the environment in a closed tomato greenhouse due to the reasons ofunreasonable design, backward equipment, incomplete control system and the like in the traditional tomato greenhouse environment can be effectively solved; and furthermore, the control problem that the tomato greenhouse environment temperature is greatly influenced due to the fact that the existing tomato greenhouse environment monitoring system does not monitor and predict the temperature in thetomato greenhouse environment according to the characteristics of nonlinearity and large lag of tomato greenhouse environmental temperature change, large tomato greenhouse area, complex temperature change and the like can be effectively solved.

The present invention discloses a degradation prediction method based on quantum attention cyclic encoding and decoding neural network (QAREDNN). QAREDNN is used in it, and a quantum attention mechanism is introduced to reconstruct the encoder and decoder at the same time so that QAREDNN can fully mine and Emphasize important information while suppressing the interference of redundant information to obtain better nonlinear approximation capabilities; use quantum neurons to construct activity values ​​and weights, and replace encoders and decoders with quantum threshold recurrent units (QGRU) replaced by quantum rotation matrices The traditional recurrent unit in the device can improve the generalization ability and response speed of QAREDNN; in the training process of QAREDNN, the LM algorithm is introduced to realize the rapid update of the rotation angle and attention parameters of the quantum rotation matrix. Due to the advantages of QAREDNN in terms of nonlinear approximation ability, generalization ability, response and training speed, etc., the degradation prediction method based on quantum attention loop encoding and decoding neural network can obtain higher prediction accuracy and computational efficiency.

The invention relates to a trend prediction method based on a double hidden layer quantum circuit recurrent unit neural network(DHL-QCRUNN). The trend prediction method comprises the steps of: constructing a permutation entropy set of original operation data; inputting the permutation entropy set into DHL-QCRUNN for training and prediction to obtain a predicted permutation entropy set; constructing a permutation entropy error set of predicted values and actual values at each time point; inputting the permutation entropy error set into DHL-QCRUNN for training and prediction to obtain a predicted and normalized permutation entropy error set; and performing denormalization processing on the predicted and normalized permutation entropy error set to obtain a final prediction result. The invention proposes a novel quantum neural network: the double hidden layer quantum circuit recurrent unit neural network. The trend prediction method updates network parameters of the DHL-QCRUNN by adoptingan LM algorithm to improve the convergence performance of the neural network, compared with other artificial intelligence methods, the DHL-QCRUNN has better nonlinear approximation ability, generalization property and faster convergence speed, the trend prediction method is used for predicting a running trend of a monitored object, and the trend prediction achieves high prediction precision, prediction stability and calculation efficiency.

The present invention discloses a method for improving interpolation position accuracy of a cutting bed based on an improved BP neural network. Parameters of a current node are input into a trained BP neural network interpolation model to obtain predicted values of the node parameters output by the BP neural network interpolation model. The predicted values of the node parameters output by the BP neural network interpolation model are substituted into a B-spline curve to obtain a predicted interpolation node. The deviation between the predicted interpolation node and the actual interpolation node and the corresponding feedback compensation output are calculated. The feedback compensation output is compared with an original machining trajectory curve; the node parameters for the next interpolation are optimized and predicted with the Newton path searching method; the predicted node parameters are substituted into the fitted B-spline curve to obtain the actual interpolation node; and the interpolation operation is performed. The method of the invention has the advantages of good nonlinear approximation ability, high interpolation precision and fast convergence speed.