Staff Members - Benha University
Neuron Analysis of the Two-Point Singular Boundary Value Problems Arising in the Thermal Explosion’s Theory
Neural Processing Letters • 2022
Publication Information
Authors
Zulqurnain Sabir1 · Hafiz Abdul Wahab1 · Mohamed R. Ali2,3 · R. Sadat4
Keywords
Thermal explosion’s theory · Singular model · Genetic algorithm · Complexity
analysis · Numerical simulations
Journal
Neural Processing Letters
Publisher
Not Available
Volume
2022
Issue
Not Available
Pages
Not Available
publication.type
International
Paper Link
Open Link
Supplementary Materials
Not Available
Abstract
artificial neural networks (US-ANNs) for solving a class of two-point nonlinear singular
boundary value problems (TPN-SBVPs) arising in the thermal explosion’s theory. The analysis
using small and large neurons (3, 10 and 30 neurons) is presented along with the absolute
error performances and complexity cost. An error function is optimized using the global and
local search mechanisms called genetic algorithm (GA) and active-set approach (ASA) for
solving the TPN-SBVPs. The correctness of the designed scheme US-ANNs using the hybrid
combination of GA-ASA is approved through the comparison of obtained and true solutions.
Moreover, statistical analysis will also be performed to authenticate the reliability and competency
of the proposed method for solving the singular model.
boundary value problems (TPN-SBVPs) arising in the thermal explosion’s theory. The analysis
using small and large neurons (3, 10 and 30 neurons) is presented along with the absolute
error performances and complexity cost. An error function is optimized using the global and
local search mechanisms called genetic algorithm (GA) and active-set approach (ASA) for
solving the TPN-SBVPs. The correctness of the designed scheme US-ANNs using the hybrid
combination of GA-ASA is approved through the comparison of obtained and true solutions.
Moreover, statistical analysis will also be performed to authenticate the reliability and competency
of the proposed method for solving the singular model.