Cerebellum model articulation controller (CMAC) is an effective localized associative memory that is capable of fast learning and low computational cost. However, it lacks interpretability and has a quantized structure. By incorporating fuzzy logic with CMAC, it gives CMAC interpretable rules and also creates a non-uniform quantized structure. However predefined dimension boundary has to be defined during CMAC creation hence implicitly enforcing a rigid structure. Fuzzy interpolation and extrapolation (FIE) have recently been widely used for sparse rule bases. With the capability of FIE, not only the number of rules used in CMAC can be greatly reduced, it also allows extrapolation with a fuzzy result beyond the rigid CMAC predefined dimension boundary. Encouraging results are discussed in the experimental section.
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