Neural network committees optimized with evolutionary methods for steel temperature control

Abstract

This paper presents regression models based on an ensemble of neural networks trained on different data that negotiate the final decision using an optimization approach based on an evolutionary approach. The model is designed for big and complex datasets. First, the data is clustered in a hierarchical way and then using different level of cluster and random choice of training vectors several MLP networks are trained. At the test phase, each network predicts an output for the test vector and the final output is determined by weighing outputs of particular networks. The weights of the outputs are determined by an algorithm based on a merge of genetic programming and searching for the error minimum in some directions. The system was used for prediction the steel temperature in the electric arc furnace in order to shorten and decrease the costs of the steel production cycle. © 2011 Springer-Verlag Berlin Heidelberg.