Learning for Cellular Neural Networks Using the GARPLA Algorithm
DOI:
https://doi.org/10.54097/cjFl3ZOQKeywords:
Edge Detection, Recurrent Perceptron Learning Algorithm (RPLA), Genetic Algorithm (GA), Weight, Cellular Neural Networks (CeNNs)Abstract
The main content of this article is to build a hybrid algorithm between genetic algorithm and Recurrent Perceptron Learning Algorithm to determine the weight set for standard cellular neural networks. In particular, the Recurrent Perceptron Learning Algorithm was announced by Gukzelis in 1998 to determine the weight set for CeNNs, however, the nature of the algorithm built according to LMS learning rules means there is still a possibility of local convergence problems of algorithm. Meanwhile, the genetic algorithm originates from natural selection problems and is capable of determining the local optimal domain of the problem. Therefore, the GARPLA hybrid algorithm will fully determine the optimal weight set for the CeNNs network, reducing potential local optimization problems encountered in the RPLA algorithm.
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