AN ASYMMETRIC ROBUST LEARNING ALGORITHM OF FUZZY CLUSTERING NEURAL NETWORKS

Demonstrated by simulations and analysis are weaknesses of the current learning algorithm of fuzzy clustering neural networks FCNN, such as initial condition sensitiveness, micro scale vibration, inflexible attracting basin and unreasonable convergence points. Furthermore, it is pointed out that the causes of the above weakness mainly lie in the symmetry of the algorithm and the lack of cooperation among the modifications of net weights. In order to eliminate the above weaknesses, the inner layer feedback is introduced, the structure of the algorithm is improved, and a robust algorithm AFC is proposed. The result of simulations verifies the proposal properly.