Pruning of Rule Base of a Neural Fuzzy Inference Network

In this work. Neural Fuzzy Inference Network (NFIN) controller is implemented that has a number of membership functions and parameters that are tuned using Genetic Algorithms. The number of rules used to define the Neuro-Fuzzy controller is then pruned. Pruning is utilized effectively to eliminate irrelevant rules in the rule base, thus keeping only the relevant rules. Pruning is performed at various threshold levels without affecting the system performance. This methodology is implemented for Water Bath System and analysis has been carried out to investigate the effect of pruning using a multi-step reference input signal. From the results, it is concluded that reasonably good performance of controller can be obtained with lesser number of rules, thus, reducing the computational complexity of the network.