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Paper details
Number 4 - December 2021
Volume 31 - 2021
A modified particle swarm optimization procedure for triggering fuzzy flip-flop neural networks
Piotr A. Kowalski, Tomasz Słoczyński
Abstract
The aim of the presented study is to investigate the application of an optimization algorithm based on swarm intelligence to
the configuration of a fuzzy flip-flop neural network. Research on solving this problem consists of the following stages. The
first one is to analyze the impact of the basic internal parameters of the neural network and the particle swarm optimization
(PSO) algorithm. Subsequently, some modifications to the PSO algorithm are investigated. Approximations of trigonometric
functions are then adopted as the main task to be performed by the neural network. As a result of the numerical
verification of the problem, a set of rules are developed that can be helpful in constructing a fuzzy flip-flop type neural
network. The obtained results of the computations significantly simplify the structure of the neural network in relation to
similar conditions known from the literature.
Keywords
fuzzy neural network, fuzzy flip-flop neuron, particle swarm optimization, training procedure, regression