Fuzzy clustering for multiple-model approaches in system identification and control. (Q2751116)

A review of fuzzy clustering and its use in the data-driven construction of nonlinear models and controllers is given. The focus is on algorithms of the fuzzy \(c\)-means type. Two application examples are presented: automated design of operating points for gain scheduling in flight control systems and nonlinear black-box identification. In the latter case, a comparison with an alternative technique is given. It is shown that fuzzy clustering is an effective technique for the decomposition of a complex nonlinear problem into a set of simpler local problems.NEWLINENEWLINEFor the entire collection see [Zbl 0966.00017].