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Local self-adaptive WNN (Wavelet Neural Network) training system, device and method

A technology of wavelet neural network and local self-adaptation, applied in self-adaptive control, general control system, control/regulation system, etc., can solve the problem of increased computational complexity and achieve the effect of reducing the amount of calculation

Inactive Publication Date: 2014-03-26
XUZHOU NORMAL UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The manifold learning algorithm, like the linear projection method, relies on the calculation of the similarity matrix, but its computational complexity does not increase significantly

Method used

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  • Local self-adaptive WNN (Wavelet Neural Network) training system, device and method
  • Local self-adaptive WNN (Wavelet Neural Network) training system, device and method
  • Local self-adaptive WNN (Wavelet Neural Network) training system, device and method

Examples

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no. 1 example

[0106] figure 1 A schematic structural diagram of a local adaptive wavelet neural network training system according to the first embodiment of the present invention is shown.

[0107] The local adaptive wavelet neural network training system is composed of offline WNN training module connected by signal and online updating WNN module.

[0108] The offline WNN training module mainly establishes the WNN initial model.

[0109] The steps are as follows: firstly cluster the training data set by using the online satisfactory G-K fuzzy clustering method, and determine the parameters of multiple wavelet functions according to the clustering results. The wavelet function scale parameters and translation parameters are randomly generated according to the cluster center, radius and variance results, and these wavelet node functions form the WNN hidden node candidate set; then use the existing WNN training algorithm to establish the initial WNN model as the current WNN.

[0110] The on...

no. 2 example

[0117] figure 2A schematic structural diagram of a locally adaptive wavelet neural network training device according to the second embodiment of the present invention is shown.

[0118] The local adaptive wavelet neural network training equipment consists of a signal-connected data preprocessing module, an online satisfactory G-K fuzzy clustering module, a wavelet function parameter setting module, a WNN update strategy selection module, a hidden node selection module, and an extended Kalman (EKF) It consists of training module, Laplacian regularization LSSVM module, experimental design Optimum module, sample addition WNN weight update module, sample removal WNN weight update module, and WNN prediction module.

[0119] The function and role of the data preprocessing module: the input parameter of the function is a data set, and the output parameter is a normalized data set.

[0120] The functions and functions of the online satisfaction G-K fuzzy clustering module:

[0121]...

no. 3 example

[0153] image 3 A method flow chart of a locally adaptive wavelet neural network training method according to the third embodiment of the present invention is shown.

[0154] The local adaptive wavelet neural network training method includes:

[0155] S31. Online local adaptive WNN structure adjustment;

[0156] S32. Updating the WNN weight online;

[0157] S33. The WNN updates the selection strategy.

[0158] The S31, online local adaptive WNN structure adjustment, specifically includes:

[0159] S311. Select WNN hidden nodes;

[0160] S312. Control the complexity of the WNN model.

[0161] S311. Select WNN hidden nodes, that is, gradually increase the hidden nodes of WNN and adjust node parameters until the fitting error meets the preset threshold; specifically include:

[0162] The online local adaptive WNN adjustment method is used to overcome the mismatch between the WNN model and the actual system caused by the nonlinear structure change of the system due to the tr...

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Abstract

The invention relates to a local self-adaptive WNN training system, device and method. The method comprises the steps of online local self-adaptive WNN structure adjustment; online WNN weight update; and WNN update strategy selection. According to the invention, a WNN model is updated online, it is ensured that the model can be popularized, the problems, which are caused by different uncertain factors and condition change of the system in practical, in WNN model adaption are overcome, and the operational stability of the system is improved, the fluctuation of production quality is lowered and service life of equipment is prolonged when the system, device and method are applied to industrial process control.

Description

technical field [0001] The invention relates to a local adaptive wavelet neural network training system, equipment and method, in particular to a local adaptive wavelet neural network training system, equipment and method with high system stability. Background technique [0002] There are sample points, and its input-output relationship is represented by a wavelet neural network (WNN) model (1) [0003] Here, is the radial basis wavelet function, its form is, and are respectively the translation parameter and the scale parameter, and is the hidden node number of WNN. When training WNN, first establish a wavelet neuron candidate set, the wavelet neuron parameters in the candidate set, and the initial value of the wavelet function parameters are determined by the results of data set clustering. For details, please refer to [Stephen A. Billings, Hua-Liang Wei. A New class of wavelet networks for nonlinear system identification. IEEE Transaction on Neural Networ...

Claims

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Application Information

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IPC IPC(8): G05B13/04
Inventor 任世锦凌萍倪银龙王高峰杨茂云吕俊怀
Owner XUZHOU NORMAL UNIVERSITY