Parameter tuning of fuzzy neural networks by immune algorithm

Shows that auto tuning of membership functions and weights in fuzzy neural networks can be effectively performed by immune algorithms. A number of hybrid methods in fuzzy-neural networks are considered in the context of tuning of learning methods, a general view is provided that they are the special cases of either the membership functions or the gain modification in the neural networks by genetic algorithms. Simulation results reveal that immune algorithms are effective approaches to search for optimal or near optimal fuzzy rules and weights