Samara Fatima, Zulqurnain Sabir, Dumitru Baleanu, Sharifah E. Alhazmi
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Gudermannian Neural Networks for Two-Point Nonlinear Singular Model Arising in the Thermal-Explosion Theory
The goal of this research is to design the Gudermannian neural networks (GNNs) to solve a type of two-point nonlinear singular boundary value problems (TPN-SBVPs) that arise within thermal-explosion theory. The results of these investigation are provided for different neurons (4, 12 and 20), as well as absolute error along with the time complexity. For solving the TPN-SBVPs, a genetic algorithm (GA) and sequential quadratic programming (SQP) are used to optimize the error function. The accuracy of designed GNNs is provided by using a hybrid GA–SQP combination, which is based on a comparison of obtained and actual solutions. Furthermore, statistical analysis of the data is proposed in order to establish the competence as well as effectiveness of designed and the efficacy of the designed computing framework for solving the TPN-SBVPs.
期刊介绍：
Neural Processing Letters is an international journal publishing research results and innovative ideas on all aspects of artificial neural networks. Coverage includes theoretical developments, biological models, new formal modes, learning, applications, software and hardware developments, and prospective researches.
The journal promotes fast exchange of information in the community of neural network researchers and users. The resurgence of interest in the field of artificial neural networks since the beginning of the 1980s is coupled to tremendous research activity in specialized or multidisciplinary groups. Research, however, is not possible without good communication between people and the exchange of information, especially in a field covering such different areas; fast communication is also a key aspect, and this is the reason for Neural Processing Letters
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