An intelligent control system combined with fuzzy reasoning and neural networks

Based on the analysis of the approach of fuzzy reasoning and a discussion of the deficiencies of the earlier developed fuzzy reasoning systems, a fuzzy reasoning model driven by neural networks for intelligent control is presented to concentrate on the task of learning control rules. In this model, the unsupervised learning technique of the connectionist learning approach is used to learn the control rules to improve the adaptive part of the fuzzy control. Using this model an intelligent control system based on the rule can be constructed. The general linear time-varying system and the nonlinear bounded time varying system are used as a test bed to demonstrate the effectiveness of the proposed control scheme and the robustness of the fuzzy control system