Support vector machines for the fuzzy neural networks
Jin-Tsong Jeng, Tsu-Tain Lee · 2003
We propose support vector machines (SVM) to improve the simplified fuzzy inference system for the fuzzy neural network. Firstly, we apply SVM to determine the number of simplified fuzzy inference system rules. Because training a SVM is equivalent to solving a linear constrained quadratic programming problem under a fixed structure of SVM, we can easily determine the number of simplified fuzzy inference system rules. Secondly, we use the solution of the SVM as initial weights in fuzzy neural networks. Based on these initial weights, the fuzzy neural networks have a fast convergent speed. Finally, we derive a learning algorithm for the proposed structure and apply the proposed method to approximate a nonlinear function. Simulation results are provided to show the validity and applicability of the developed method.