A combined approach to fuzzy model identification

A combined approach for discrete-time fuzzy model identification is proposed. By this approach, the identification is performed in two stages. First, the linguistic approach is utilized to obtain an approximate fuzzy relation from the sampled nonfuzzy input-output data. This approximate fuzzy relation is then used as the initial estimate for the second stage in which a more accurate fuzzy relation is determined by the approach of numerical resolution of fuzzy relational equation. A recursive identification algorithm based on the prediction-error method is derived for optimally resolving the numerical fuzzy relational equation by minimizing a quadratic performance index. This algorithm makes the proposed approach particularly attractive to online applications. Two numerical examples are provided to show the superiority of the combined approach over other methods