Mamdani fuzzy parameter estimation algorithm for large-scale interconnected nonlinear systems

For the category of large-scale nonlinear processes that are composed of a set of linked monovariable systems and represented by discrete input-output models with unknown time-varying coefficients, the current study proposes a recursive algorithm of maximum likelihood estimation based on the fuzzy inference technique. This recursive estimator employs a prediction error strategy and a maximum likelihood estimation algorithm to formulate the issue of estimating the parameters of the process under consideration. The established parameter estimation approach is enhanced by the addition of the Mamdani fuzzy inference system. A numerical simulation exemplar is used to verify the efficacy of the generated theoretical results..