Initial structure selection for neural networks and fuzzy neural networks based on support vector regression with outliers

A new support vector regression (SVR) algorithm based on the statistical method is proposed to select an initial structure for the certain neural networks and fuzzy neural networks. There are repeated SVR methods to use in the proposed method. Because the SVR approach is equivalent to solve a linear constrained quadratic programming problem the number of hidden nodes and adjustable parameters in the proposed networks are easily obtained. Based on the better initial structure for the proposed networks, the convergence epoch of the proposed networks are faster than the conventional neural networks and fuzzy neural networks with BP learning algorithm and robust BP learning algorithm. Simulation results are provided to show the validity and applicability of the proposed method.