Adaptive learning rate wavelet neural network control method based on normalization lowest mean square adaptive filtering

Abstract

The invention relates to the technical field of wavelet neural network optimization, in particular to an adaptive learning rate wavelet neural network control method based on normalization lowest mean square adaptive filtering. The adaptive learning rate wavelet neural network control method comprises the steps that a control system model is built; unitization is conducted on all weight values of a wavelet network by layer; wavelet neural cell weight value optimization is carried out; an error signal and training cost are figured out; segment processing is conducted on derived functions of an activation function through a step function; fuzzy rules of fitting the derived functions are made; a membership function is determined; the proportion of each fuzzy rule in a derived function value is determined; a fuzzy system is output, and the activation function is displayed in a linearization mode; induction local areas of all neural cells are determined, and the neural cells are output; each local gradient function is solved; adjustment of the learning rate is conducted by an output layer in an adaptive mode; the range of the learning rate of the output layer is determined; the learning rate of a hidden layer is adjusted; neural cell synapse weight values are trained; a tracking control signal is output; closed-loop feedback control is completed. According to the adaptive learning rate wavelet neural network control method based on normalization lowest mean square adaptive filtering, the rate of convergence can be increased, and computation complexity can be reduced.

Description

technical field [0001] The invention relates to the technical field of wavelet neural network optimization, in particular to an adaptive learning rate wavelet neural network control method based on normalized least mean square adaptive filtering. Background technique [0002] Complex systems are often uncertain, and the nonlinear functions inside the system are difficult to establish, so the method based on system structure cannot be used to realize the tracking control of complex systems. The artificial neural network is a network system formed by the interconnection of artificial neurons. It abstracts and simplifies the human brain from the microstructure and function, and can be regarded as a large-scale highly parallel processor composed of simple processing units. , naturally has the characteristics of storing empirical knowledge and making it available. The similarity between the neural network and the human brain is that the knowledge acquired by the neural network i...

AI Technical Summary

Problems solved by technology

[0003] The limitations of LMS and NLMS in the weight derivation process are mainly manifested in that they are only suitable for linear structures, and the nonlinear activation function of wavelet network will make this process very complicated.

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