Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
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A novel approach is presented for continuous function approximation using a two-stage neural network model involving Support Vector Machines (SVM) and an adaptive unsupervised Neural Network to be applied to real functions of many variables. It involves an adaptive Kohonen feature map (SOFM) in the first stage which aims at quantizing the input variable space into smaller regions representative of the input space probability distribution and preserving its original topology, while rapidly increasing, on the other hand, cluster distances. During convergence phase of the map a group of Support Vector Machines, associated with its codebook vectors, is simultaneously trained in an online fashion so that each SVM learns to respond when the input data belong to the topological space represented by its corresponding codebook vector. The proposed methodology is applied, with promising results, to the design of a neural-adaptive controller, by involving the computer-torque approach, which combines the proposed two-stage neural network model with a servo PD feedback controller. The results achieved by the suggested SVM approach are favorably compared to the ones obtained if the role of SVMs is undertaken, instead, by Radial Basis Functions (RBF).