Incremental and stable training algorithm for wind turbine neural modeling
Keywords:
wind turbine, neural models, incremental algorithm, adaptive learning rateAbstract
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.Downloads
Published
Issue
Section
License
Engineering review uses the Creative Commons Attribution-NonCommercial-NoDerivatives (CC-BY-NC-ND) 4.0 International License, which governs the use, publishing and distribution of articles by authors, publishers and the wider general public.
The authors are allowed to post a digital file of the published article, or the link to the published article (Enginering Review web page) may be made publicly available on websites or repositories, such as the Author’s personal website, preprint servers, university networks or primary employer’s institutional websites, third party institutional or subject-based repositories, and conference websites that feature presentations by the Author(s) based on the published article, under the condition that the article is posted in its unaltered Engineering Review form, exclusively for non-commercial purposes.
The journal Engineering Review’s publishing procedure is performed in accordance with the publishing ethics statements, defined within the Publishing Ethics Resource Kit. The Ethics statement is available in the document Ethics Policies.