Open AccessProceedings Article
Artificial neural network based prediction of optimal pseudo-damping and meta-damping in oscillatory fractional order dynamical systems
Saptarshi Das,Indranil Pan,Khrist Sur,Shantanu Das +3 more
- pp 350-356
TLDR
In this paper, the authors investigated typical behaviors like damped oscillations in fractional order (FO) dynamical systems and used a multilayer feed-forward ANN to predict the optimal pseudo and meta-damping from knowledge of the maximum order or number of terms in the FO dynamical system.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.read more
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Journal ArticleDOI
Original Contribution: Multilayer feedforward networks with a nonpolynomial activation function can approximate any function
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Posted Content
Multilayer feedforward networks with non-polynomial activation functions can approximate any function
Moshe Leshno,Shimon Schocken +1 more
TL;DR: It is shown that a standard multilayer feedforward network can approximate any continuous function to any degree of accuracy if and only if the network's activation functions are not polynomial.
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TL;DR: In this article, a modern approach to solve the solvable system of fractional and other differential equations, linear, non-linear; without perturbation or transformations, but by applying physical principle of action-and-opposite-reaction, giving approximately exact series solutions.