Combining Back-Propagation and Genetic Algorithms to Train Neural Networks for Ambient Temperature Modeling in Italy

Authors:
Francesco Ceravolo;Matteo Felice;Stefano Pizzuti
Affiliations:
Energy, New technology and Environment Agency (ENEA), Rome, Italy 00123;Energy, New technology and Environment Agency (ENEA), Rome, Italy 00123 and Department of Informatics and Automation, University of Rome "Roma 3", Rome, Italy 00146;Energy, New technology and Environment Agency (ENEA), Rome, Italy 00123
Venue:
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
Year:
2009

Citing 5
Cited 1

Adaptation in natural and artificial systems

Adaptation in natural and artificial systems
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence

Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
The Handbook of Brain Theory and Neural Networks

The Handbook of Brain Theory and Neural Networks
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning

The application of an artificial immune system-based back-propagation neural network with feature selection to an RFID positioning system

Robotics and Computer-Integrated Manufacturing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a hybrid approach based on soft computing techniques in order to estimate ambient temperature for those places where such datum is not available. 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 initialises a few individuals of the GA's population. Experiments have been performed over all the available Italian places and results have shown a remarkable improvement in accuracy compared to the single and traditional methods.