A fast learning algorithm for parsimonious fuzzy neural systems
In this paper, a novel learning algorithm for dynamic fuzzy neural networks based on extended radial basis function neural networks, which are functionally equivalent to Takagi-Sugeno-Kang fuzzy systems, is proposed. The algorithm comprises 4 parts: (1) criteria of rules generation; (2) allocation of premise parameters; (3) determination of consequent parameters and (4) pruning technology. The salient characteristics of the approach are: (1) a hierarchical on-line self-organizing learning paradigm is employed so that not only parameters can be adjusted, but also the determination of structure can be self-adaptive without partitioning the input space a priori; (2) fast learning speed can be achieved so that the system can be implemented in real time. Simulation studies and comprehensive comparisons with some other learning algorithms demonstrate that the proposed algorithm is superior in terms of simplicity of structure, learning efficiency and performance. (C) 2002 Elsevier Science B.V. All rights reserved.
fast learning fuzzy neural networks hierarchical on-line self-organizing learning tsk fuzzy reasoning