Abstract
The conventional CMAC (Cerebellar Model Articulation Controller) neural network (NN) can be applied in many real-world applications thanks to its high learning speed and good generalization capability. In this paper it is proposed to utilize a neuro-evolutional approach to adjust CMAC parameters and construct mathematical models of nonlinear objects in the presence of the gaussian noise. The general structure of the evolving CMAC NN is considered. The paper demonstrates that the evolving CMAC NN can be used effectively for the identification of nonlinear dynamical systems. The simulation of the proposed approach for various nonlinear objects is performed. The results proved the effectiveness of the developed methods.
Highlights
Using a mathematical model of the cerebellar cortex developed by D
Albus proposed a model describing the motion control processes that occur in the cerebellum, which was subsequently implemented in the neural network controller for controlling the robot - arm, which he called CMAC - Cerebellar Model Articulation Controller [2, 3]
It should be noted that in designing a network CMAC a number of difficulties in the selection of parameters such as the number of levels and the quantization levels, the shape of the receptive field, the type of applied information hashing algorithm and training
Summary
Using a mathematical model of the cerebellar cortex developed by D. It should be noted that in designing a network CMAC a number of difficulties in the selection of parameters such as the number of levels and the quantization levels, the shape of the receptive field, the type of applied information hashing algorithm and training. Unlike most optimization algorithms designed to solve a problem, EA operate with a multitude of solutions - the population, which allows reaching a global minimum, without getting stuck in the local ones In this case, information about each individual of the population is encoded in a chromosome (genotype), and the solution (phenotype) is obtained after evolution (selection, crossing, mutation) by decoding. GA abstract the fundamental processes of Darwinian evolution: natural selection and genetic changes due to recombination and mutation
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