Incremental and stable training algorithm for wind turbine neural modeling

Authors

  • Slim Abid Control & Energy Management Lab (CEM LAB) National School of Engineering of Sfax, B.P. 1173, 3038 Sfax, University of Sfax, Tunisia
  • Mohamed Chtourou Control & Energy Management Lab (CEM LAB) National School of Engineering of Sfax, B.P. 1173, 3038 Sfax, University of Sfax, Tunisia
  • Mohamed Djemel Control & Energy Management Lab (CEM LAB) National School of Engineering of Sfax, B.P. 1173, 3038 Sfax, University of Sfax, Tunisia

Keywords:

wind turbine, neural models, incremental algorithm, adaptive learning rate

Abstract

Training and topology design of artificial neuralnetworks are important issues with largeapplication. This paper deals with an improvedalgorithm for feed forward neural networks (FNN) straining. The association of an incrementalapproach and the Lyapunov stability theoryaccomplishes both good generalization and stabletraining process. The algorithm is tested on windturbine modeling. Compared to the incrementalapproach and to the Lyapunov stability basedmethod, the association of both strategies givesinteresting results.

Author Biographies

Slim Abid, Control & Energy Management Lab (CEM LAB) National School of Engineering of Sfax, B.P. 1173, 3038 Sfax, University of Sfax, Tunisia

Mohamed Chtourou, Control & Energy Management Lab (CEM LAB) National School of Engineering of Sfax, B.P. 1173, 3038 Sfax, University of Sfax, Tunisia

Mohamed Djemel, Control & Energy Management Lab (CEM LAB) National School of Engineering of Sfax, B.P. 1173, 3038 Sfax, University of Sfax, Tunisia

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Published

2013-09-03

Issue

Engineering Review, Vol. 33, No. 3

Section

Articles

License

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