A KIND OF THE FEEDFORWARD AND BIG SAMPLES ALGORITHM WITH ROBUSTNESS AND SMOOTHNESS

In this paper the weight factors and the confidence interval are introduced into the energy function of the neural networks, the reliability and robustness for training samples are improved. In the same way, the smoothing property of approaching function is considered, the curvature is added in the energy function, the feedforward algorithm with robustness and smoothness for big samples is proposed. The improvements are followed, the approaching function has the smoothness and the algorithm fits for the occasion of a great of samples, the simulation results prove the effectiveness of the algorithm.