Abstract
A new fuzzy inference model, "SIRMs(Single Input Rule Modules) Connected Fuzzy Inference Model", for plural input fuzzy control is proposed. In the model, the degree of importance is defined first and single input fuzzy rule module is constructed for each input item. Then, the final output is obtained by summarizing the products of the importance degree and the fuzzy inference result of each module. When the number of input items increases, the total number of fuzzy rules rises only algebraically for the proposed model while ascends exponentially for the conventional model. Moreover, the importance degrees can be strengthened or weakened according to experts' intuitive experiences to achieve each control purpose. The proposed model is applied to typical first-order lag systems and second-order lag systems to confirm the improvement in control performance compared with the conventional models. Tuning algorithm is also given based on the simplified inference method. Finally, the proposed model is applied to identification of non-linear functions of 4 inputs. The results show that the proposed model has the ability to identify non-linear systems, too.
