Modular structure of fuzzy system modeling using wavelet networks

This paper proposes a novel approach to constructing fuzzy logic control system using the combination of wavelet network and feedforward BP networks. Numerous works have been reported on the integration, of fuzzy logic systems and neural networks; however most of these hybridizations have resulted in the loss of the original knowledge structure embedded in its network implementation. Due to the inherent modularity, the decision-making process of a fuzzy logic control system can be readily partitioned into three functionally independent modules, namely pattern recognition, fuzzy reasoning and control synthesis, and therefore individual subnets can be built to implement these modules as subnets for the overall feedforward networks. Both wavelet network and BP feedforward network are adopted for the construction of the subnets so that the local receptive field-type wavelet networks with orthogonal least square learning algorithm are capable to capture the fine details of the underlying dynamics, and the overall feedforward structure with the BP algorithm is responsible for the global optimization of the networks parameters. Simulation also shows its superiority.