Abstract
It is an open problem to model nonlinear systems with uncertainties. In Chapter 2, we developed an identification algorithm based on the Takagi-Sugeno fuzzy model. The fuzzy modeling procedure in Chapter 2 can be divided into three steps: premise structure identification, premise parameters identification, and consequent parameters identification. The premise structure identification procedure is done in two phases: (1) Identify the input structure, i.e., the significant input variables are identified among all possible input candidates; (2) assign fuzzy membership functions. In Chapter 2, we introduced an identification algorithm which included both phases in a uniform processes. We can also deal with them in two individual processes.
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(2006). Fuzzy Model Identification Based on Rough Set Data Analysis. In: Fuzzy Modeling and Fuzzy Control. Control Engineering. Birkhäuser Boston. https://doi.org/10.1007/978-0-8176-4539-7_3
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DOI: https://doi.org/10.1007/978-0-8176-4539-7_3
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