| Title | Combining back-propagation and genetic algorithms to train neural networks for start-up time modeling in combined cycle power plants |
|---|---|
| Publication Type | Presentazione a Congresso |
| Year of Publication | 2010 |
| Authors | Bertin, I., De Felice Matteo, and Pizzuti S. |
| Conference Name | Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010 |
| Keywords | Backpropagation 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. |
| URL | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84887014383&partnerID=40&md5=747e62cfedc84c4b0b513a4e83276638 |
| Citation Key | Bertin2010165 |
