CMAC-based neuro-fuzzy approach for complex system modeling

A cerebellar model arithmetic computer (CMAC)-based neuron-fuzzy approach for accurate system modeling is proposed. The system design comprises the structure determination and the hybrid parameter learning. In the structure determination, the CMAC-based system constitution is used for structure initialization. With the advantage of generalization of CMAC, the initial receptive field constitution is formed in a systematic way. In the parameter learning, the random optimization algorithm (RO) is combined with the least square estimation (LSE) to train the parameters, where the premises and the consequences are updated by RO and LSE, respectively. With the hybrid learning algorithm, a compact and well-parameterized CMAC can be achieved for the required performance. The proposed work features the following salient properties: (1) good generalization for system initialization; (2) derivative-free parameter update; and (3) fast convergence. To demonstrate potentials of the proposed approach, examples of SISO nonlinear approximation, MISO time series identification/prediction, and MIMO system mapping are conducted. Through the illustrations and numerical comparisons, the excellences of the proposed work can be observed.