Improved inference for Takagi-Sugeno models

Some undesirable properties of the standard Takagi-Sugeno (TS) inference method are discussed in relation to an analysis of the TS model and its approximation accuracy. A new inference method based on a smoothing maximum function is proposed. This method guarantees smoothness of the model output to a desired degree, boundedness of the output gradient at each point by the local gradients of the rule consequences and also improves the accuracy of the TS model, as demonstrated in the given numerical examples.

[1]  Donald Gustafson,et al.  Fuzzy clustering with a fuzzy covariance matrix , 1978, 1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes.

[2]  Israel Zang,et al.  A smoothing-out technique for min—max optimization , 1980, Math. Program..

[3]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[4]  R.J. Hathaway,et al.  Switching regression models and fuzzy clustering , 1993, IEEE Trans. Fuzzy Syst..

[5]  R. Jager,et al.  Interpolation issues in Sugeno-Takagi reasoning , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.