A combined SVM regression methodology for modelling robotic manipulators inverse dynamics
نویسنده
چکیده
A novel approach is presented for approximating real continuous functions of many variables using a two-stage neural network technique. In the first stage, a group of Support Vector Machines (SVMs) is involved in approximating the function based on different methods for selecting and transforming the input variables. In the second stage, an SVM is employed to combine the function approximation decisions of the previous stage’s group of SVMs. Different kinds of Artificial Neural Networks are used at this stage, including SVM, Multilayer Perceptron (MLP) and Perceptron. Their performance is compared with simple averaging and other statistical methods for combining the outputs of many regressors. The proposed methodology is applied to the design of a neural-adaptive controller which combines the two-stage neural network model with a servo PD feedback controller. The different feature extraction methods involved in this case include the Fourier as well as the Wavelet transform of the input variables, which are samples belonging in the mappings of position, velocity and acceleration as functions of time.
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تاریخ انتشار 2003