Embedded support vector regression on Cerebellar Model Articulation Controller with Gaussian noise
In this study, an approach utilizing support vector regression (SVR) as the learning scheme of a Cerebellar Model Articulation Controller (CMAC) to handle noisy data is proposed. This approach is referred to as SVR-CMAC. Firstly, the memory-associated vector is transformed via the SVR model. Then, the output is computed from the SVR model as a given input of a CMAC. That is, the memory size of the proposed SVR-CMAC depends on the number of support vectors. It is difference from the conventional CMAC and the kernel CMAC that mainly depends on the number of input variables. Secondly, in order to measure the distance between two memory-associated vectors (i.e. unipolar binary input data), the modified Hamming distance is used in the proposed SVR-CMAC. That is, the modified Hamming distance measure is incorporated into the kernel function in the SVR model. Furthermore, the existed SVR software is easily modified to implement the SVR approach with these new Gaussian kernel functions. Besides, some easy approaches to determine the hyperparameters of the proposed SVR-CMAC are also proposed. Consequently, the proposed SVR-CMAC solves once a linearly constrained quadratic programming problem to obtain the final results. However, the final results of the conventional CMAC and the kernel CMAC need to update the weights with iteration. Finally, from the simulation results, the performance of the proposed SVR-CMAC is better than the conventional CMAC and the kernel CMAC for noisy data. (C) 2010 Elsevier B.V. All rights reserved.
support vector regression cerebellar model articulation controller controller kernel cerebellar model articulation