Adaptive Learning-Rate Backpropagation Neural Network Algorithm Based on the Minimization of Mean-Square Deviation for Impulsive Noises

This paper presents a novel adaptive learning-rate backpropagation neural network (ALR-BPNN) algorithm based on the minimization of mean-square deviation (MSD) to implement a fast convergence rate and robustness to impulsive noises. The learning rates of the weights in each hidden layer are derived to minimize the upper bound of the MSD obtained by the analysis, which guarantees a fast convergence rate in a stable range. Moreover, by adopting the variance of the kind of the measurement noises in each layer through the variance of the error signals, the proposed scheme provides robustness to the impulsive noises. The performance of the proposed algorithm is evaluated on various sequential signals and industrial data including the impulsive noise and compared with conventional ALR-BPNN algorithms. Simulation results indicate that the proposed algorithm outperforms the existing algorithms.