Abstract
For traditional fuzzy inference modeling methods, model parameters are selected based on experience, the model is also determined, which has the different dynamic tracking capability for different systems, so has bad generalization ability. Considering the limitations of conventional fuzzy inference modeling, here proposes a kind of Gaussian-type membership function, and proves that adaptive fuzzy inference system based on Gaussian-type membership can approximate nonlinear systems at arbitrary precision. Design of adaptive fuzzy inference system structure and parameters adjustment program, adopt particle swarm algorithm to optimize model parameters, the simulation results show the effectiveness of the proposed method.
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