59 results about "Nonlinear approximation" patented technology

A vehicle dynamics behavior reproduction system capable of describing accurately behavior of a motor vehicle in a lateral direction even for nonlinear driving situation includes a vertical wheel force arithmetic means (105), a lateral wheel force arithmetic means (110), a cornering stiffness adaptation means (115), a state space model / observer unit (120), a selector (130), a delay means (135), and a tire side slip angle arithmetic means (125). Vertical wheel forces (FZij) and tire side slip angles (αij) are determined by using sensor information and estimated values while lateral wheel forces (FYij) are determined in accordance with a relatively simple nonlinear approximation equation. The lateral wheel force (FYij) and the tire side slip angle (αij) provide bases for adaptation of cornering stiffnesses at individual wheels. Vehicle motion is accurately described to a marginal stability by using adapted cornering stiffnesses (Cij) and other information.

The invention relates to a heterogeneous cloud wireless access network resource allocation method based on deep reinforcement learning, and belongs to the technical field of mobile communication. Themethod comprises the following steps: 1) taking queue stability as a constraint, combining congestion control, user association, subcarrier allocation and power allocation, and establishing a random optimization model for maximizing the total throughput of the network; 2) considering the complexity of the scheduling problem, the state space and the action space of the system are high-dimensional,and the DRL algorithm uses a neural network as a nonlinear approximation function to efficiently solve the problem of dimensionality disasters; and 3) aiming at the complexity and the dynamic variability of the wireless network environment, introducing a transfer learning algorithm, and utilizing the small sample learning characteristics of transfer learning to enable the DRL algorithm to obtain an optimal resource allocation strategy under the condition of a small number of samples. According to the method, the total throughput of the whole network can be maximized, and meanwhile, the requirement of service queue stability is met. And the method has a very high application value in a mobile communication system.

A non-linear dynamic predictive device (60) is disclosed which operates either in a configuration mode or in one of three runtime modes: prediction mode, horizon mode, or reverse horizon mode. An external device controller (50) sets the mode and determines the data source and the frequency of data. In the forward modes (prediction and horizon), the data are passed to a series of preprocessing units (20) which convert each input variable (18) from engineering units to normalized units. Each preprocessing unit feeds a delay unit (22) that time-aligns the input to take into account dead time effects. The output of each delay unit is passed to a dynamic filter unit (24). Each dynamic filter unit internally utilizes one or more feedback paths that provide representations of the dynamic information in the process. The outputs (28) of the dynamic filter units are passed to a non-linear approximator (26) which outputs a value in normalized units. The output of the approximator is passed to a post-processing unit (32) that converts the output to engineering units. This output represents a prediction of the output of the modeled process. In reverse horizon mode, data is passed through the device in a reverse flow to produce a set of outputs (64) at the input of the predictive device. These are returned to the device controller through path (66). The purpose of the reverse horizon mode is to provide information for process control and optimization. The predictive device approximates a large class of non-linear dynamic processes. The structure of the predictive device allows it to be incorporated into a practical multivariable non-linear Model Predictive Control scheme, or used to estimate process properties.

A constrained non-linear approximator for empirical process control is disclosed. The approximator constrains the behavior of the derivative of a subject empirical model without adversely affecting the ability of the model to represent generic non-linear relationships. There are three stages to developing the constrained non-linear approximator. The first stage is the specification of the general shape of the gain trajectory or base non-linear function which is specified graphically, algebraically or generically and is used as the basis for transfer functions used in the second stage. The second stage of the invention is the interconnection of the transfer functions to allow non-linear approximation. The final stage of the invention is the constrained optimization of the model coefficients such that the general shape of the input / output mappings (and their corresponding derivatives) are conserved.

The invention provides a motor-vehicle tail-gas concentration prediction method of an LSTM-based deep space-time residual-network. Powerful data nonlinear-approximation capability and self-learning capability of a deep learning method are utilized for establishing an LSTM-based depth-space residual-network structure, space-time prediction of urban motor-vehicle tail-gas concentration is realized,urban motor-vehicle tail-gas space-time data and external influence factor data are collected, the urban motor-vehicle tail-gas space-time data are used for storage, training and prediction, two-dimensional prediction analysis of time and space is directly carried out, external conditions of weather, holidays and the like are considered at the same time, and a fully-connected two-layer network isused for simulating external influence factor characteristics to enable a prediction result to be more comprehensive and accurate.

A constrained non-linear approximator for empirical process control is disclosed. The approximator constrains the behavior of the derivative of a subject empirical model without adversely affecting the ability of the model to represent generic non-linear relationships. There are three stages to developing the constrained non-linear approximator. The first stage is the specification of the general shape of the gain trajectory or base non-linear function which is specified graphically, algebraically or generically and is used as the basis for transfer functions used in the second stage. The second stage of the invention is the interconnection of the transfer functions to allow non-linear approximation. The final stage of the invention is the constrained optimization of the model coefficients such that the general shape of the input / output mappings (and their corresponding derivatives) are conserved.

The invention discloses a distribution prediction method of Mn and associated heavy metals, such as Cu, Ni, Zn, Pb, Cd and the like, in a soil-water interface contaminated flow in a manganese mine area. On the basis of runoff water sample actual detection data of a sampling point of a soil-water interface in the manganese mine area and along-river distribution status of heavy metal content in sediments, a BP prediction model of the manganese and associated heavy metal distribution in the soil-water interface contaminated flow in the manganese mine area is constructed on the basis of a GIS technology through a geostatistic method with the GIS technology with a BP neural network introduced. The predict model has excellent fault-tolerant capability and nonlinear approximating mapping capability. A predict result of contents and spatial distribution of the manganese and associated heavy metal of the soil-water interface contaminated flow in manganese mine area is more objective and accurate.

The invention discloses a calibrating method for an electronic compass. The calibrating method is used for promoting measuring accuracy of the electronic compass based on a self-adaption differential evolution algorithm and a Fourier neural network theory and is especially suitable for an orienting system with low cost and higher precision. The calibrating method comprises the following steps: utilizing a Fourier neural network to perform error modeling on the electronic compass and utilizing an improved self-adaption differential evolution algorithm to optimize a weight value of the Fourier neural network, so as to acquire an accurate error model to compensate a measuring value of the electronic compass. The error model which is established according to the calibrating method is capable of realizing accurate mapping of a sample space and has a higher nonlinear approaching capability. According to the calibrating method, a minimum local part is avoided, the defects of over-slow convergence rate and oscillation of the neural network are overcome and the influence of an outside magnetic field on the electronic compass is efficiently compensated, thereby greatly promoting the measuring accuracy of the electronic compass.

The invention discloses a linear pseudo-spectrum GNEM guidance and control method. According to the linear pseudo-spectrum GNEM guidance and control method, an original nonlinear optimal control problem is converted into a group of problems for solving a system of linear algebraic equations based on the concept of GNEM through linearization and Gauss pseudo-spectrum dispersion by combining nonlinear approximation model predictive control, linear quadratic optimal control and a Gauss pseudo-spectrum method. The linear pseudo-spectrum GNEM guidance and control method has the advantages that the calculation efficiency of solving the optimal control problem is quite high, and high calculation precision can be obtained just through several nodes; in addition, a final solution can be presented through a smooth function relevant to control over disperse nodes, and the method is quite suitable for online calculation. It is indicated through a simulation result that by applying the method to tail-section attack guidance with terminal angle constraint, compared with an MPSP method, the linear pseudo-spectrum GNEM guidance and control method has higher calculation efficiency and high calculation precision and can be completely applicable to a guidance framework for terminal guidance, and a smaller required overload command is generated compared with self-adaptive tail-section proportion guidance.

The invention discloses a fusing method of an atmospheric attack angle and an inertia attack angle in transonic flight stage. The fusing method comprises the following steps of: firstly, data acquisition; secondly, transonic flight stage judgment; thirdly, inertia attack angle solution; fourthly, complementary filtering fusing; and fifthly, BP neural network correction. Through a complementary filter, the method inhibits impact wave interference fluctuation of the atmospheric attack angle, and enables complementary fused attack angles to eliminate fierce fluctuation and become stable. Further, by utilizing a favorable learning ability and a non-linear approximation property of the neural network, the method ensures a better anastomosis degree between a real attack angle and a final fused modified attack angle which is treated by the neural network, and enables whole precision and reliability of the attack angle to be improved further. By replacing a primary atmospheric attack angle in the transonic flight stage with the fused modified attack angle, the invention can effectively improve whole precision of the attack angle during flight, and provides a beneficial foundation of accurate navigation and flight control.

The invention discloses a fault diagnosis method based on deep convolution domain adversarial transfer learning, and the method comprises the steps: carrying out high-level feature extraction in a DCDATL through employing a deep convolution residual feature extractor, and improving the convergence and nonlinear approximation capability of the DCDATL; acquiring feature joint distribution representation through an obtained Kronecker product of high-level features and label information and is embedded into a domain classifier, and training domain adversarial to improve the migration performance of the DCDATL. The feature migration and classification process of the joint distribution domain confrontation overall loss function based on the minimized DCDATL improves the classification precisionafter migration. Due to the advantages of the DCDATL, the fault diagnosis method based on the DCDATL can perform high-precision fault diagnosis on the current to-be-detected sample of the rotating machine by utilizing the labeled sample under the historical working condition under the condition that the labeled sample under the current working condition of the rotating machine does not exist.

Methods and apparatuses for measuring a light absorbance are provided. The method measures light absorbance of at least one detection chamber of a microfluidic device, including the detection chamber and at least one reference chamber. The detection chamber may accommodate a test subject. The method includes detecting a plurality of reference transmitted light intensities for the at least one reference chamber and estimating a value between the plurality of reference transmitted light intensities through nonlinear approximation. The estimated value is then applied to light absorbance measurement of the detection chamber to reduce a light absorbance error of the detection chamber.

The invention discloses a neural network adaptive trajectory tracking control method for a permanent magnet synchronous linear motor and belongs to the field of linear motor motion control technologies. The method utilizes the nonlinear mapping capability of a neural network controller to establish model estimation and compensation links of the permanent magnet synchronous linear motor, so that good trajectory tracking performance of the motor is realized. The control method comprises two parts including a neural network controller based on a radial basis function (RBF) and a robust controller with feedback gain used for carrying out model estimation and compensation and restraining the influence of a model error respectively. According to the method, the nonlinear approximation characteristic of the neural network is utilized to establish the nonlinear compensation link of the model of the permanent magnet synchronous linear motor, so that the influence of nonlinear factors, such as dead zone and thrust ripple, in a motor operation process are effectively overcome, the method has stronger anti-interference capability and good stability, and the good trajectory tracking performance can be realized.

A method of providing a low cost quantized nonlinear scaler for a continuous curve includes the steps of providing a quantized coefficient table representing an approximation of the nonlinear continuous curve; applying the table in hardware to locate coefficients in the table, and applying the coefficients to a scaling pipeline to get scaled data. The nonlinear approximation in the table is achieved by following the rules that the sampled data must be consistent, the quantization points must be consistent, the coefficient curve must be symmetric about its centerline, and the quantization values must be selected so that the sum of all coefficients values in the coefficient curve over all the source points for a given resultant point is always equal to a normalization value.

The invention provides a non-affine and nonlinear fault-tolerant controller design method for a non-affine and nonlinear system with disturbance and parameter uncertainty. The designed observer can be applied to the nonlinear system with non-affine parameters, and the observer has ideal robustness in the presence of the parameters of large-range variation. Fault information and disturbance information are hidden in the observer. A fault-tolerant controller is dynamically designed based on the observer. Due to the fact that the system is the non-affine and nonlinear system, the design of the controller is not easy. The invention provides the dynamic non-affine and nonlinear approximation method. The non-affine and nonlinear system is approximated into an affine nonlinear system with time-varying parameters. Parameters which need to be known are estimated online through a filter. A non-affine flight control system is used to verify the effectiveness of the provided method. The robust fault-tolerant control of the non-affine and nonlinear system can be realized.

The invention discloses an error compensation method for a photoelectric encoder. Based on improved particle swarm optimization and a Fourier neural network principle, the method is used for improving the measurement accuracy of the photoelectric encoder, and is particularly suitable for an angle measuring system requiring low cost and high accuracy. According to the method, a compass error is modeled by a Fourier neural network, and the weight of the neural network is optimized by the improved particle swarm optimization, so that an accurate error model is obtained to compensate a measured value of the photoelectric encoder. The error model established by the method can realize accurate mapping of a sample space and has high nonlinear approximation capability; and by the method, local minimum is avoided, a defect that the neural network has ultralow convergence rate, oscillates and the like is overcome, measurement errors of the photoelectric encoder are effectively reduced, and the measurement accuracy of the photoelectric encoder is greatly improved.

A method of processing geophysical signals obtained by monitoring the response of the earth to an source using a plurality of receivers is described including the evaluation of sums or integrals of functions of weighted signal values over a one or multidimensional domain such that the domain is split into a plurality of simplices and the signal values are interpolated across the simplices using a non-linear approximation of the function, the approximation including signals and gradients of the signals, and the evaluated sums or integrals are used to obtain a representation of characteristics of the earth.

The invention discloses a remote sensing data-based high-precision agricultural region ground surface temperature retrieval method, which comprises the following steps: firstly, calculating relationship between thermal radiation intensity and temperature, and performing non-linear approximation on Planck function by utilizing exponential function; and on the basis, carrying out derivation of a high-precision retrieval method of the ground surface temperature of the agricultural region by combining ground surface thermal radiation transmission equation. According to the method, the ground surface emissivity and atmospheric transmittance are required: NDVI of the ground surface of the agricultural region can be extracted by utilizing the data of ASTER visible light waveband and near infrared waveband so as to further calculate the emissivity data; the content of atmospheric water vapor can be retrieved by utilizing the data of MODIS near infrared waveband, and accurate atmospheric transmittance data can be obtained by fitting the relationship between atmospheric transmittance and the content of atmospheric water vapor through utilizing segmental cubic polynomial fitting. The ground surface temperature of the agricultural region can be accurately obtained, the space-time distribution of the ground surface temperature can be accurately analyzed, the space difference of the regional temperature can be analyzed, thus providing basic data for agriculture, weather, hydrology, ecology, biogeochemistry and the like.

The invention discloses a decision feedback model-based digital symbol nonlinear error correction equalization method, which comprises the following steps: processing a signal received by a receiver by utilizing a feed-forward delay register, and rapidly calculating a front-path interference cancellation signal; processing an output signal of a data decision module by utilizing a feedback delay register, and rapidly calculating a back-path interference cancellation signal; performing subtraction on the front-path interference cancellation signal and the back-path interference cancellation signal to obtain an interference cancellation signal, and processing the signal to obtain a transmitted symbol by virtue of the data decision module; obtaining a final equalizer coefficient by utilizing an expected signal of the symbol, a current interference cancellation signal and signals before and after the expected signal and the current interference cancellation signal. According to the method, increase of complexity is avoided, inter-code interference and inter-channel interference of communication channels can be rapidly cancelled, high nonlinear approximation property of the interference cancellation signal and low complexity of a minimum mean square error-based equalization coefficient updating scheme are utilized, and the method has the advantages of accuracy, stability and high efficiency in high-carrier high-bandwidth wireless communication.

The invention discloses a convolutional-fuzzy neural network method for actively controlling global spatial noise of a vehicle. The method comprises the steps of arranging a plurality of secondary paths around a vehicle noise reduction area; acquiring a noise residual signal of each secondary path; adopting a convolutional-fuzzy neural network to firstly perform offline identification to obtain asecondary path model and then serve as an adaptive active noise control algorithm of the secondary path at the same time to correct controller parameters online; and finally, outputting multi-azimuthnoise cancellation signals. According to the invention, the convolutional-fuzzy neural network is used for identifying the inverse model of an object, a very effective method is provided for vehicle global space nonlinear noise identification, and the identification precision of the secondary path is improved by utilizing the nonlinear approximation capability of the convolutional-fuzzy neural network to a function; an active feedback noise elimination system is adopted, and a stable secondary path model is established; and the problems that vehicle global space noise is difficult to control and the frequency band is narrow are solved.

The invention discloses a composite orthogonal neural network prediction control-based intelligent ship tracking method. The method comprises the steps of: obtaining a predetermined trajectory in themovement process of a ship, and calculating optimization algorithm predicted thrust of each propeller through an optimization algorithm according to the predetermined trajectory and predicted outputs;carrying out weighted stacking on the optimization algorithm predicted thrust and neural network predicted thrust through the neural network predicted thrust so as to output thrust to be generated byeach propeller; predicting the position, heading and speed of the ship through a prediction model; and correcting predicted values of the position, heading and speed of the ship and taking the corrected predicted values as the predicted outputs. According to the method, a composite orthogonal neural network is combined to put forward a new model prediction strategy; the neural network is simple in algorithm and high in learning convergence speed, and has the excellent characteristics of high linear and nonlinear approximation accuracy and the like; and the learning algorithm of the neural network can be offline completed, so that the online calculation time is greatly shortened.

The invention aims to provide a ship salt-containing sewage treatment control prediction system and prediction method based on a wavelet neural network. The wavelet theory and the neural network are combined, so that the wavelet neural network completely inherits the excellent time-frequency localization characteristic of wavelet transformation and the self-learning characteristic of the neural network, and the strong nonlinear approximation capability is realized. The problems that a traditional neural network prediction model is poor in precision, low in stability and the like are solved particularly aiming at the characteristics of high nonlinearity, strong coupling, time varying, large hysteresis and complexity in the salt-containing sewage treatment process. A corresponding control strategy is provided, self-repairing is achieved while ship sewage treatment equipment is self-monitored and diagnosed, compared with other algorithms, the intelligent degree is high, and the operationcost is further saved. Experimental results show that the prediction method can well predict the pollutant removal efficiency in the high-salinity seawater, so that a feasible operation strategy is provided for the treatment of the ship salt-containing sewage.

Methods and apparatuses for measuring a light absorbance are provided. The method measures light absorbance of at least one detection chamber of a microfluidic device, including the detection chamber and at least one reference chamber. The detection chamber may accommodate a test subject. The method includes detecting a plurality of reference transmitted light intensities for the at least one reference chamber and estimating a value between the plurality of reference transmitted light intensities through nonlinear approximation. The estimated value is then applied to light absorbance measurement of the detection chamber to reduce a light absorbance error of the detection chamber.

The invention discloses a calibrating method for an electronic compass. The calibrating method is used for promoting measuring accuracy of the electronic compass based on a self-adaption differential evolution algorithm and a Fourier neural network theory and is especially suitable for an orienting system with low cost and higher precision. The calibrating method comprises the following steps: utilizing a Fourier neural network to perform error modeling on the electronic compass and utilizing an improved self-adaption differential evolution algorithm to optimize a weight value of the Fourier neural network, so as to acquire an accurate error model to compensate a measuring value of the electronic compass. The error model which is established according to the calibrating method is capable of realizing accurate mapping of a sample space and has a higher nonlinear approaching capability. According to the calibrating method, a minimum local part is avoided, the defects of over-slow convergence rate and oscillation of the neural network are overcome and the influence of an outside magnetic field on the electronic compass is efficiently compensated, thereby greatly promoting the measuring accuracy of the electronic compass.

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.

The invention discloses an urban rail vehicle wheel set curve fitting method. According to the method, wheel set surface data information points are acquired, wherein wheel set surface data information is acquired through two-dimensional laser displacement sensors arranged at the two sides of a rail; the surface data information is preprocessed, wherein detected wheel set surface data is preprocessed, and a reconstructed wheel set surface curve is obtained; a to-be-fitted curve segment is determined, wherein a to-be-fitted region on a wheel set curve is determined according to the reconstructed wheel set surface curve; wheel set curve fitting is performed, wherein a least square support vector machine method is adopted to perform fitting processing on the wheel set surface curve within the to-be-fitted region range, and a complete wheel set surface curve is obtained; and wheel set dimension parameters are calculated, wherein the wheel set dimension parameters are obtained through calculation according to wheel set dimension parameter definition criteria. Through the method, the two-dimensional laser displacement sensors are used to acquire the wheel set surface data information, the least square support vector machine method is used to convert a nonlinear approximation problem into a linear approximation problem, therefore, the fitting effect is good, and the calculation result is accurate.