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.
|Publication status||Published - 2010|
|Event||18th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2010 - , Belgium|
Duration: 1 Jan 2010 → …
|Conference||18th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2010|
|Period||1/1/10 → …|
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Information Systems
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.