A self-organizing recurrent fuzzy CMAC model for dynamic system identification

This paper presents a self-organizing recurrent fuzzy cerebellar model articulation controller (RFCMAC) model for identifying a dynamic system. The recurrent network is embedded in the RFCMAC by adding feedback connections with a receptive field cell to the RFCMAC, where the feedback units act as memory elements. A nonconstant differentiable Gaussian basis function is used to model the hypercube structure and the fuzzy weight. An online learning algorithm is proposed for the automatic construction of the proposed model during the learning procedure. The self-constructing input space partition is based on the degree measure to appropriately determine the various distributions of the input training data. A gradient descent learning algorithm is used to adjust the free parameters. The advantages of the proposed RFCMAC model are summarized as follows: (1) it requires much lower memory requirement than other models; (2) it selects the memory structure parameters automatically; and (3) it has better identification performance than other recurrent networks.