AI techniques in modelling, assignment, problem solving and optimization

Authors:
Zsolt János Viharos;Zsolt Kemény
Affiliations:
Computer and Automation Research Institute, Hungarian Academy of Sciences, H-1111, Kende u. 13-17, Budapest, Hungary;Computer and Automation Research Institute, Hungarian Academy of Sciences, H-1111, Kende u. 13-17, Budapest, Hungary
Venue:
Engineering Applications of Artificial Intelligence
Year:
2007

Citing 2
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Training and Application of Artificial Neural Networks with Incomplete Data

IEA/AIE '02 Proceedings of the 15th international conference on Industrial and engineering applications of artificial intelligence and expert systems: developments in applied artificial intelligence
A dynamic architecture for artificial neural networks

Neurocomputing

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Abstract

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.