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

I. Bertin, M. De Felice, S. Pizzuti

Research output: Contribution to conferencePaper

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.
Original languageEnglish
Publication statusPublished - 2010
Externally publishedYes
Event18th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2010 - , Belgium
Duration: 1 Jan 2010 → …

Conference

Conference18th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2010
CountryBelgium
Period1/1/10 → …

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All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Information Systems

Cite this

Bertin, I., De Felice, M., & Pizzuti, S. (2010). Combining back-propagation and genetic algorithms to train neural networks for start-up time modeling in combined cycle power plants. Paper presented at 18th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2010, Belgium.