Two suggestions for efficient implementation of CMAC´s

The CMAC (Cerebellar Model Articulation Controller) suffers from two important problems: the huge amount of memory needed for its implementation in many common situations, and the lack of a systematic way for selecting appropriate values for its parameters, particularly number of quantization intervals. This paper presents two proposals for addressing these difficulties: 1) a dynamic implementation that requires memory only for those weights needed to represent the training data set, and that performs linear interpolation when a query using other weights is requested; and 2) consists of the definition of an index of correlation from which the optimal number of quantization intervals that should be assigned to each dimension of the input space that can be found. Experiments are performed for two synthetic cases and for one set of real data. These are used to model the dynamic behaviour of a real sensor-based car. Figures are given to show the memory savings and mean squared error obtained