In this article we developed a method for optimizing the structure of a fuzzy artificial neural networks (FANN) through genetic algorithms. This genetic algorithm (GA) is used by optimizing the number of weight connections in a neural network structure, by the evolution of those structures as individuals in a population. It is found that the optimization of the neural network provides higher confidence accuracy of the suggested solution in a Case Based Diagnostic System. The computational cost of the optimized network also improved considerably high.
|Number of pages||7|
|Journal||Proceedings of SPIE - The International Society for Optical Engineering|
|Publication status||Published - 2000|
|Event||Applications and Science of Computational Intelligence III - Orlando, FL, USA|
Duration: 24 Apr 2000 → 27 Apr 2000