1996 Volume 8 Issue 3 Pages 488-498
Fuzzy modeling is a method to describe the characteristics of complex systems using fuzzy inference. The method has a distinguishing feature in that it can express complex nonlinear systems linguistically. However in the case where the system has multi-inputs, the acquired fuzzy rules through some sorts of learning are hard to understand. And when it is difficult to obtain sufficient input-output data from the system with multi-inputs, the fuzzy model cannot be made precise. The authors have proposed a hierarchical fuzzy modeling method. The proposed fuzzy modeling method uses fuzzy neural networks(FNNs). The identified fuzzy rules with the new method are easy to understand. The method enables to obtain a precise fuzzy model even with limited number of input-output data. However, there have been some cases where this conventional method was trapped into local minima to model some nonlinear objects. This paper presents a new hierarchical fuzzy modeling using multiple submodels. The new fuzzy modeling method, having the features of the hierarchical fuzzy modeling method, expands the exploratory space for fuzzy models. This proposed method allows multiple submodels in a large and increases the possibility to obtain more adequate fuzzy models. Sumulations using numerical data are done to show the effectiveness of the proposed method.
