Artificial Neural Network Based Prediction of Optimal Pseudo-Damping and Meta-Damping in Oscillatory Fractional Order Dynamical Systems

TL;DR

This paper uses genetic algorithms and neural networks to predict optimal damping parameters in fractional order dynamical systems, enhancing understanding and control of their oscillatory behavior.

Contribution

It introduces a novel approach combining GA and ANN to accurately predict pseudo and meta-damping in fractional order systems, which was not previously available.

Findings

ANN can predict damping parameters effectively

GA provides accurate damping approximations

Method improves control of FO system oscillations

Abstract

This paper investigates typical behaviors like damped oscillations in fractional order (FO) dynamical systems. Such response occurs due to the presence of, what is conceived as, pseudo-damping and meta-damping in some special class of FO systems. Here, approximation of such damped oscillation in FO systems with the conventional notion of integer order damping and time constant has been carried out using Genetic Algorithm (GA). Next, a multilayer feed-forward Artificial Neural Network (ANN) has been trained using the GA based results to predict the optimal pseudo and meta-damping from knowledge of the maximum order or number of terms in the FO dynamical system.