A novel design of fractional Mayer wavelet neural networks with application to the nonlinear singular fractional Lane-Emden systems
Ver/
Compartir
Estadísticas
Ver Estadísticas de usoMetadatos
Mostrar el registro completo del ítemÁrea de conocimiento
Matemática AplicadaPatrocinadores
This paper is partially supported by Ministerio de Ciencia, Innovación y Universidades grant number PGC2018-097198-BI00 and Fundación Séneca de la Región de Murcia grant number 20783/PI/18.Fecha de publicación
2021Editorial
ElsevierCita bibliográfica
Sabir, Zulqurnain et al. “A novel design of fractional Meyer wavelet neural networks with application to the nonlinear singular fractional Lane-Emden systems.” alexandria engineering journal 60 (2021): 2641-2659.Palabras clave
Meyer wavelet kernelsNeural networks
Hybrid computing techniques
Lane-Emden equation
Singular systems
Stochastic computing
Resumen
In this study, a novel stochastic computational frameworks based on fractional Meyer wavelet artificial neural network (FMW-ANN) is designed for nonlinear-singular fractional Lane-Emden (NS-FLE) differential equation. The modeling strength of FMW-ANN is used to transformed the differential NS-FLE system to difference equations and approximate theory is implemented in mean squared error sense to develop a merit function for NS-FLE differential equations. Meta-heuristic strength of hybrid computing by exploiting global search efficacy of genetic algorithms (GA) supported with local refinements with efficient active-set (AS) algorithm is used for optimization of design variables FMW-ANN., i.e., FMW-ANN-GASA. The proposed FMW-ANN-GASA methodology is implemented on NS-FLM for six different scenarios in order to exam the accuracy, convergence, stability and robustness. The proposed numerical results of FMW-ANN-GASA are compared with exact solutions to verify the correctness, viability and ...
Colecciones
- Artículos [1752]
El ítem tiene asociados los siguientes ficheros de licencia:
Redes sociales