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Intelligent computing to solve fifth-order boundary value problem arising in induction motor models
- Source :
- Neural Computing and Applications. 29:449-466
- Publication Year :
- 2016
- Publisher :
- Springer Science and Business Media LLC, 2016.
-
Abstract
- In this study, biologically inspired intelligent computing approached based on artificial neural networks (ANN) models optimized with efficient local search methods like sequential quadratic programming (SQP), interior point technique (IPT) and active set technique (AST) is designed to solve the higher order nonlinear boundary value problems arise in studies of induction motor. The mathematical modeling of the problem is formulated in an unsupervised manner with ANNs by using transfer function based on log-sigmoid, and the learning of parameters of ANNs is carried out with SQP, IPT and ASTs. The solutions obtained by proposed methods are compared with the reference state-of-the-art numerical results. Simulation studies show that the proposed methods are useful and effective for solving higher order stiff problem with boundary conditions. The strong motivation of this research work is to find the reliable approximate solution of fifth-order differential equation problems which are validated through strong statistical analysis.
- Subjects :
- 0209 industrial biotechnology
Mathematical optimization
Artificial neural network
business.industry
Differential equation
02 engineering and technology
Transfer function
020901 industrial engineering & automation
Artificial Intelligence
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Local search (optimization)
Boundary value problem
business
Software
Interior point method
Induction motor
Sequential quadratic programming
Mathematics
Subjects
Details
- ISSN :
- 14333058 and 09410643
- Volume :
- 29
- Database :
- OpenAIRE
- Journal :
- Neural Computing and Applications
- Accession number :
- edsair.doi...........e27986e1ecb60f380e151a0eb8198583