This article is dedicated to study the impact of machine intelligence (MI) methods viz. various
types of Neural models for investigating dynamical systems arising in interdisciplinary areas …

The concept of oscillator problems finds its indispensable presence in numerous dynamical
systems. Machine learning techniques for handling dynamical systems is a challenging and …

In view of the fact that impulsive differential equations have the discreteness due to the
impulse phenomenon, this article proposes a single hidden layer neural network method …

Differential equations play a significant role in modeling of real world dynamical problems. A
large amount of prior physical information in the form of differential equations are inherited in …

In this chapter, a neural network model for solving fractional differential equations is
described. This model is based on approximating the solution by using power series …

The machine learning technique for handling multimodal dynamic system is a challenging
and rapidly expanding field of research. Study of linear and non-linear systems of singular …

A Chebyshev polynomial neural network for solving boundary value problems for one-and
two-dimensional partial differential equations is constructed. In particular, the input …

Dynamical problems are generally governed by a set of linear/non-linear differential
equations (DEs). A large amount of prior physical information in the form of DEs plays an …

Among the metrological controls, the control of the roughness of a surface, this
measurement activity has a cost that should not be underestimated in the parts …