AI techniques in modelling, assignment, problem solving and optimization

This paper recapitulates the results of a long research on a family of artificial intelligence (AI) methods—relying on, e.g., artificial neural networks and search techniques—for handling systems with high complexity, high number of parameters whose input or output nature is partly unknown, high number of dependencies, as well as uncertainty and incomplete measurement data. Aside from classical modelling, basic problem solving and optimization techniques are presented. Finally, a novel submodel decomposition method is shown with an extended feature selection algorithm highlighted, along with possibilities of further development. Examples of practical application are shown to illustrate the viability of the methods. r 2006 Elsevier Ltd. All rights reserved.

• Type: Journal
• Year: 2007
• Where: EAAI
• Authors: Zsolt János Viharos, Zsolt Kemény
• Keywords: Artificial Neural Networks, EAAI 2007, Extended Feature Selection, Incomplete Measurement Data