Optimizing fuzzy knowledge base by genetic algorithms and neural networks

Fuzzy logic invented by L.A. Zadeh has been used to handle and represent information which is vague, uncertain and imprecise. Many fuzzy control systems and expert systems have been developed to capture operator knowledge and domain expert knowledge respectively. Fuzzy production rules (FPRs) have been used to capture and represent domain expert knowledge for years. To draw an accurate, reasonable and reliable conclusion in a fuzzy expert system, the knowledge base plays an important role and is the heart of this system. Once a fuzzy expert system (FES) has been built, we are faced with a large number of parameters which need to be tuned in order to improve the system performance in terms of the results (conclusions) obtained. Many approaches have been proposed to tune the parameters of this system. The parameters include membership functions, weights (local and global), and certainty factors, etc. In this paper, a method is proposed to tune some of these parameters using a genetic algorithm (GA) and a neural network (NN). The neural network is used to model and capture parameters on its connection weights and provide initial values of these parameters for the genetic algorithm to optimize. The result of such tuning is that the overall system performance is greatly improved and the tuning task could be done automatically. A fuzzy expert system which provides expert advice for computer professionals and computer science graduates in selecting an appropriate job is used to test the proposed method.