This paper presents a neural networks based approach in order to estim ate 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.
['Ilaria Bertini', 'Matteo De Felice', 'Stefano Pizzuti']
Combining Back-Propagation and Genetic Algorithms to Train Neural Networks for Start-Up Time Modeling in Combined Cycle Power Plants