Artificial Neural Network Based Prediction of Optimal Pseudo-Damping and Meta-Damping in Oscillatory Fractional Order Dynamical Systems
This work addresses modeling challenges in fractional order systems for researchers in control theory or applied mathematics, but it appears incremental as it combines existing methods like GA and ANN for a specific application.
The paper tackled predicting optimal pseudo-damping and meta-damping in fractional order dynamical systems by using a Genetic Algorithm to approximate damped oscillations and training an Artificial Neural Network on these results, achieving predictions based on system order or terms.
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.