<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

A modified particle swarm optimization procedure for triggering fuzzy flip-flop neural networks

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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...

A modified particle swarm optimization procedure for triggering fuzzy flip-flop neural networks

Online veröffentlicht: 30. Dez. 2021

Seitenbereich: 577 - 586

Eingereicht: 28. Apr. 2021

Akzeptiert: 07. Sept. 2021

DOI: https://doi.org/10.34768/amcs-2021-0039

Schlüsselwörter
fuzzy neural network, fuzzy flip-flop neuron, particle swarm optimization, training procedure, regression

© 2021 Piotr A. Kowalski et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A modified particle swarm optimization procedure for triggering fuzzy flip-flop neural networks

Online veröffentlicht: 30. Dez. 2021

Seitenbereich: 577 - 586

Eingereicht: 28. Apr. 2021

Akzeptiert: 07. Sept. 2021

DOI: https://doi.org/10.34768/amcs-2021-0039

Schlüsselwörterfuzzy neural network, fuzzy flip-flop neuron, particle swarm optimization, training procedure, regression

© 2021 Piotr A. Kowalski et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
fuzzy neural network, fuzzy flip-flop neuron, particle swarm optimization, training procedure, regression