An Optimizing Algorithm of Recursive Orthogonal Least Squares Based on RBF Nets

This paper discusses rival penalized competitive learning and proposes the recursive orthogonal least squares algorithm. The use of modified Givens rotations avoids orthogonal decompositioin of complex matrices. In case of satisfying measure accuracy, backward selection method reduces the architecture of RBF networks. Simulation results show that algorithms can effectively calculate the number of hidden layer nodes on network learning without intervention and the method could be applied to nonlinear system modeling.