An incremental construction learning algorithm for identification of T-S fuzzy systems

This paper proposes an incremental construction learning algorithm for identification of T-S Fuzzy Systems. The mechanism of the algorithm is that it is an error-reducing driven learning method. Beginning with a simplest T-S fuzzy system, the algorithm develops the system structure by adding more fuzzy terms and rules to reduce the model errors in a 'greedy' way. The main features of the proposed algorithm are that, firstly, it can automatically determines and controls the number and location of fuzzy terms needed by following the error-reducing driven evolving process to achieve the desired accuracy; secondly, it adds new fuzzy terms and rules by evenly distributing error to each sub-region aiming at an efficient set of fuzzy rules, thirdly, it uses triangular membership functions and the regular partitions in constructing T-S fuzzy systems and leads to identified T-S fuzzy system models with good transparency and interpretability and suitable for advanced stability analysis and design approaches such as piecewise Lyapounov methods. Two dynamical system identification examples are given to illustrate the advantages of the proposed algorithm.