H1 Estimation for Fuzzy Membership Function Optimization

Given a fuzzy logic system, how can we determine the membership functions that will result in the best performance? If we constrain the membership functions to a speci ̄c shape (e.g., triangles or trapezoids) then each membership function can be parameterized by a few variables and the membership optimization problem can be reduced to a parameter optimization problem. The parameter optimization problem can then be formulated as a nonlinear ̄ltering problem. In this paper we solve the nonlinear ̄ltering problem using H1 state estimation theory. However, the ¤Email address: d.j.simon@csuohio.edu

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