Optimization of fuzzy-neural structure through genetic algorithms and its application in artificial odor recognition-system

Fuzzy neural networks are gaining much research interest and have attracted considerable attention recently, due to diverse applications in such fields as pattern recognition, image processing and control. However, this type of neural system, similar to that of multilayer perceptrons, has a drawback due to its huge neural connections. In this article, we proposed a method for optimizing the structure of a fuzzy artificial neural network (FANN) through genetic algorithms. This genetic algorithm (GA) is used to optimize the number of weight connections in a neural network structure, by evolutionary calculation of the fitness function of those structures as individuals in a population. The developed optimized fuzzy neural net is then applied for pattern recognition in an odor recognition system. Experimental results show that the optimized neural system provides higher recognition capability compared with that of unoptimized neural systems. The recognition rate of the unoptimized neural structure is 70.4% and could be increased to 85.2% in the optimized neural system. It is also shown that the computational cost of the optimized neural system structure is also lower than for the unoptimized structure.