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Combining back-propagation and genetic algorithms to train neural networks for start-up time modeling in combined cycle power plants

TitoloCombining back-propagation and genetic algorithms to train neural networks for start-up time modeling in combined cycle power plants
Tipo di pubblicazionePresentazione a Congresso
Anno di Pubblicazione2010
AutoriBertin, I., De Felice Matteo, and Pizzuti S.
Conference NameProceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010
Parole chiaveBackpropagation algorithms, BP algorithm, Combined cycle power plants, Genetic algorithms, Hybrid approach, Learning systems, Neural networks, Simple genetic algorithm, Startup time
Abstract

This paper presents a neural networks based approach in order to estimate the start-up time of turbine based power plants. Neural networks are trained with a hybrid approach, indeed we combine the Back-Propagation (BP) algorithm and the Simple Genetic Algorithm (GA) in order to effectively train neural networks in such a way that the BP algorithm initializes a few individuals of the GA's population. Experiments have been performed over a big amount of data and results have shown a remarkable improvement in accuracy compared to the single traditional methods.
URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84887014383&partnerID=40&md5=747e62cfedc84c4b0b513a4e83276638
Citation KeyBertin2010165