A T-S model identification method based on harmony search algorithm

The conventional T-S fuzzy model identification methods, such as the fuzzy c-means (FCM) algorithm and least-squares method, usually fail to find the optimal solutions, because they determine the consequent parameters based on only one certain group of the premise parameters. That is to say, these techniques are usually trapped into the local optima in the multidimensional parameter space. In the present paper, a new hybrid identification algorithm(HIA) is introduced to overcome the above drawback. Our method can simultaneously optimize the premise and consequent parameters by merging the harmony search algorithm(HS), FCM algorithm and least-squares method together. This hybrid approach also has the remarkable feature of error feedback mechanism. Simulation results demonstrate that the proposed optimization algorithm can effectively escape from the local optima, and yield a superior performance over the regular parameter identification methods.