An efficient algorithm for automatically generating multivariable fuzzy systems by Fourier series method

By exploiting the Fourier series expansion, we have developed a new constructive method of automatically generating a multivariable fuzzy inference system from any given sample sets with the resulting multivariable function being constructed within any specified precision to the original sample set. The given sample sets are first decomposed into a cluster of simpler sample sets such that a single input fuzzy system is constructed readily for a sample set extracted directly from the cluster independent of the other variables. Once the relevant fuzzy rules and membership functions are constructed for each of the variables completely independent of the other variables, the resulting decomposed fuzzy rules and membership functions are integrated back into the fuzzy system appropriate for the original sample set requiring only a moderate cost of computation in the required decomposition and composition processes. After proving two basic theorems which we need to ensure the validity of the ...

• Type: Journal
• Year: 2002
• Where: TSMC
• Authors: Liang Chen, Naoyuki Tokuda
• Keywords: Fuzzy Rules, Multivariable Fuzzy Inference, Sample Sets, TSMC 2002