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The Integration of Extension Theory to Design a New Fuzzy Inference Model

A novel extension-based fuzzy model is proposed in this paper. The newly established extension theory is integrated into the conventional fuzzy system to enhance the reasoning capability. In parameter identification process, adjusting a membership function to satisfy one pattern may deteriorate the others performance and result in a lengthy turning process. This incompatible issue is alleviated by the extension theory. We will investigate how to define the extended relational functions and how to refine the roughly designed model to meet the system requirement. During the refining process, both the fired and the neighborhood of the fired membership functions are adjusted simultaneously. Simulation results from single-input-single-output and double-input-single-output models verified that better results than the conventional methods have been obtained.