A multisolution learning algorithm for fuzzy rules

This paper presents a novel learning algorithm for the approximate reasoning-based Fuzzy Adaptive Mapping Network (FAMN). In the proposed algorithm, we use a serial genetic algorithm to adjust the membership function of the rules. In particular, unlike other adaptive methods, the learning is achieved by incorporating the idea of multiple resolution. The tuning is first implemented with few rules at the coarsest resolution of input/output variables, then with many rules at the higher resolution until the training precision required is obtained. The 2D sine function net is constructed as an illustrative example