FUN: optimization of fuzzy rule based systems using neural networks

A method for optimization of fuzzy rule based systems using neural networks is described. A neural network model with special neurons has been developed so that the translation of fuzzy rules and membership functions into the network is possible. The performance of this network, and hence the quality of the original rule base, is then improved by training the network using a combination of neural network learning algorithms. The optimized rules and membership functions can be extracted from the net and used in normal fuzzy inference tools. This net has been tested on the WallJumperOver and the problem of local navigation for mobile robots