An unsupervised wavelet neural network model for approximating the solutions of non-linear nervous stomach model governed by tension, food and medicine
AK Sahoo, S Chakraverty - Computer Methods in Biomechanics …, 2024 - Taylor & Francis
The human stomach is a complex organ. Its role is to degrade food particles by using
mechanical forces and chemical reactions in order to release nutrients. All ingested items …
mechanical forces and chemical reactions in order to release nutrients. All ingested items …
Stabilised auto-regressive neural networks (s-ARNNs) for data driven prediction of forced nonlinear systems
Forced vibrations of nonlinear dynamical systems are usually predicted by solving
differential equations, which have been derived from 'first principles' given by physical laws …
differential equations, which have been derived from 'first principles' given by physical laws …
The Numerical Solution of Nonlinear Fractional Lienard and Duffing Equations Using Orthogonal Perceptron
This paper proposes an approximation algorithm based on the Legendre and Chebyshev
artificial neural network to explore the approximate solution of fractional Lienard and Duffing …
artificial neural network to explore the approximate solution of fractional Lienard and Duffing …
[HTML][HTML] Design of Morlet Wavelet Neural Networks for Solving the Nonlinear Van der Pol–Mathieu–Duffing Oscillator Model
The motivation behind this study is to simplify the complex mathematical formulations and
reduce the time-consuming processes involved in traditional numerical methods for solving …
reduce the time-consuming processes involved in traditional numerical methods for solving …
Bifurcations Due to Different Neutral Delays in a Fractional-Order Neutral-Type Neural Network
This article lucubrates the bifurcations in respect of a fractional-order neutral-type neural
network (FONTNN) with two nonidentical delays. To begin with, the characteristic equation …
network (FONTNN) with two nonidentical delays. To begin with, the characteristic equation …
An Unsupervised Scientific Machine Learning Algorithm for Approximating Displacement of Object in Mass-Spring-Damper Systems
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 …
large amount of prior physical information in the form of differential equations are inherited in …
Double parametric based solution of fuzzy unconfined aquifer problem using Laplace transforms method
The Boussinesq equation describes the model for horizontal water flow in unconfined
aquifers without precipitation, a topic that has been extensively studied in the literature …
aquifers without precipitation, a topic that has been extensively studied in the literature …
An advanced scheme based on artificial intelligence technique for solving nonlinear riccati systems
Recently, one artificial intelligence technique, known as artificial neural network (ANN), has
brought advanced development to the arena of mathematical research. It competes …
brought advanced development to the arena of mathematical research. It competes …
Novel approach for solving higher-order differential equations with applications to the Van der Pol and Van der Pol–Duffing equations
Background Numerical methods are used to solve differential equations, but few are
effective for nonlinear ordinary differential equations (ODEs) of order higher than one. This …
effective for nonlinear ordinary differential equations (ODEs) of order higher than one. This …
Unsupervised ANN model for solving fractional differential equations
AK Sahoo, S Chakraverty - Computation and Modeling for Fractional Order …, 2024 - Elsevier
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 …
described. This model is based on approximating the solution by using power series …