A new scheme for an automatic generation of multi-variable fuzzy systems
We present a new novel method of automatically generating a multi-variable fuzzy inference system from the given sample sets. We first decompose the sample set, say Lambda, into a cluster of sample sets associated with the given input variables, then compute the associated fuzzy rules and membership functions for each variable, independent of the other variables, by solving a single input multiple output fUzzy system extracted from the set cluster. The resulting decomposed fuzzy rules and membership functions for all the variables are integrated back into the fuzzy system appropriate for the original sample set ii. Taking advantage of the independence of the input variables in computing the decomposed systems, we show that the computational complexity of the multi-variable system can in principle be reduced to that of a single variable if we can use a parallel processing multi-CPU system. We have verified our claim using an eight variable nonlinear function. (C) 2001 Elsevier Science B.V. All rights reserved.
fuzzy system fuzzy rule membership function parallel computation neural network