Recently, artificial neural networks (ANNs) have been widely used in many different fields. Research topics related to ANNs have proved suitable for many areas, such as control,25,26 identification,27,28 pattern recognition,29,30 equalization,31,32 and image processing.33,34 The cerebellar model articulation controller (CMAC) model proposed by Albus35,36 is usually applied in ANNs. The CMAC model imitates the structure and function of the cerebellum of a human brain and it is similar to a local network. The CMAC model can be viewed as a basis function network that uses plateau basis functions to compute the output of the model for a given input data point. Therefore, only the basis functions assigned to the hypercube covering the input data are needed. In other words, for a given input vector, only a few of the network nodes (or hypercube cells) are active and will effectively contribute to the corresponding network output. Thus, the CMAC has good learning and generalization capabilities. However, the CMAC requires a large amount of memory for solving the problem of the high dimension,37,38 is ineffective for online learning systems,39and has relatively poor function approximation ability.40,41 Another problem is that it is difficult to determine the memory structure, e.g., to adaptively select structural parameters, in the CMAC model.42,43 Recently, several researchers have proposed various solutions for the above problems, including fuzzy membership functions,44 selection of learning parameters,45 topology structure,46 spline functions,47 and fuzzy C-means.48 Fuzzy theory embedded in the CMAC model has been widely discussed. Thus, a fuzzy CMAC called FCMAC49 was proposed. It takes full advantage of the concept of fuzzy theory and combines it with the local generalization feature of the CMAC model.49,50 A recurrent network is embedded in the CMAC model by adding feedback connections with a receptive field cell to the CMAC model,51 which has the advantage of dynamic characteristics (considering past output network information). However, the above-mentioned methods have several drawbacks. For example, the mapping capability of local approximation by hyper-planes is not good enough, and more hypercube cells (rules) are required.