An Improved Levenberg-Marquardt Algorithm with Adaptive Learning Rate for RBF Neural Network

In this paper, an improved Levenberg-Marquardt (LM) algorithm with adaptive learning rate is proposed to optimize the learning process of RBF neural networks. First, an improved LM algorithm is adopted using a quasi-Hessian matrix and gradient vector which are computed directly. Compared with the conventional LM algorithm, Jacobian matrix multiplication and storage are not required in the improved LM algorithm, which can reduce computation cost and solve the problem of memory limitation. Second, the adaptive learning rate is integrated into the improved LM algorithm in order to accelerate the convergence speed of training algorithm and improve the network performance of nonlinear system modeling. Finally, several experiments are conducted and the results show that the proposed method has faster convergence speed and better prediction performance.