Neuro-fuzzy Systems Complexity Reduction by Subtractive Clustering and Support Vector Learning for Nonlinear Process Modeling

The design of a neuro-fuzzy system based on a radial basis function (RBF) network architecture and using support vector learning is considered. Typically, a neuro-fuzzy model structure is created from numerical data, however the common modeling techniques may introduce unnecessary redundancy into the rule base. It is of great interest to reduce the number of fuzzy rules. The proposed method proceeds in two phases. First, the input-output data is clustered according to a modified form of the Mountain Method for cluster estimation the subtractive clustering method. Second, a support vector machine is defined. The parameters of the network, number of centers, its positions and output layer weights are computed using support vector learning. This approach will improve the interpretability analysis and reduces the complexity of the problem. The proposed learning scheme is applied to the distributed collector field of a solar power plant.