A novel design of Gudermannian function as a neural network for the singular nonlinear delayed, prediction and pantograph differential models

The present work is to solve the nonlinear singular models using the framework of the stochastic computing approaches. The purpose of these investigations is not only focused to solve the singular models, but the solution of these models will be presented to the extended form of the delayed, predict...

Full description

Saved in:
Bibliographic Details
Published in:Mathematical biosciences and engineering : MBE Vol. 19; no. 1; pp. 663 - 687
Main Authors: Sabir, Zulqurnain, Wahab, Hafiz Abdul, Guirao, Juan L.G.
Format: Journal Article
Language:English
Published: United States AIMS Press 01.01.2022
Subjects:
Algorithms
complexity analysis
global scheme
gudermannian neural networks
local approach
Neural Networks, Computer
neuron analysis
Nonlinear Dynamics
Reproducibility of Results
singular models
statistical performances
singular models
neuron analysis
complexity analysis
global scheme
local approach
statistical performances
Gudermannian neural networks
ISSN:1551-0018, 1551-0018
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The present work is to solve the nonlinear singular models using the framework of the stochastic computing approaches. The purpose of these investigations is not only focused to solve the singular models, but the solution of these models will be presented to the extended form of the delayed, prediction and pantograph differential models. The Gudermannian function is designed using the neural networks optimized through the global scheme "genetic algorithms (GA)", local method "sequential quadratic programming (SQP)" and the hybridization of GA-SQP. The comparison of the singular equations will be presented with the exact solutions along with the extended form of delayed, prediction and pantograph based on these singular models. Moreover, the neuron analysis will be provided to authenticate the efficiency and complexity of the designed approach. For the correctness and effectiveness of the proposed approach, the plots of absolute error will be drawn for the singular delayed, prediction and pantograph differential models. For the reliability and stability of the proposed method, the statistical performances "Theil inequality coefficient", "variance account for" and "mean absolute deviation'' are observed for multiple executions to solve singular delayed, prediction and pantograph differential models.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1551-0018
1551-0018
DOI:10.3934/mbe.2022030