Self-adaptive wavelet kernel neural network tracking control method based on KLMS

Abstract

The invention discloses a self-adaptive wavelet kernel neural network tracking control method based on a KLMS. The method includes the following steps that a wavelet kernel neural network is initialized; a preset value is compared with an actual observed value output by a controlled object, an error signal is obtained and input into the wavelet kernel neural network, and a cost function is solved; the self-adaptive learning rate of a hidden layer-output layer weight is adjusted, and the hidden layer-output layer weight is updated; the self-adaptive learning rate of an input layer-hidden layer weight is adjusted, and the input layer-hidden layer weight is updated; the constriction factor of a wavelet kernel function is updated; the induction local field and the output of a hidden layer are solved; the induction local field and the output of an output layer are solved, and the output serves as control signals to be transmitted to an actuating mechanism of the controlled object. Through the self-adaptive wavelet kernel neural network tracking control method based on the KLMS, memory internal storage and computation complexity in the iteration process are reduced, and a control system is made more accurate and faster.

Description

technical field [0001] The invention belongs to the field of neural network tracking control, in particular to a KLMS-based self-adaptive wavelet kernel neural network tracking control method. Background technique [0002] The artificial neural network is a network system composed of interconnected artificial neurons. It abstracts and simplifies the human brain from the perspective of microstructure and function. It can be regarded as a large-scale highly parallel processor composed of simple processing units. Nature has the property of storing experiential knowledge and making it available. In terms of processing and computing, although the function of each processing unit seems simple, the parallel activities of a large number of simple processing units make the network present rich functions under the premise of ensuring a relatively high speed. The basic idea of ​​neural network control is to simulate the action mode of the human brain nervous system from the perspectiv...

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Problems solved by technology

The incompleteness of this basis leads to the inability to approximate any classification interface on the subspace

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