Extreme and incremental learning based single-hidden-layer regularization ridgelet network

Based on the previous work on ridgelet neural network, which employs the ridgelet function as the activation function in a feedforward neural network, in this paper we proposed a single-hidden-layer regularization ridgelet network (SLRRN). An extra regular item indicating the prior knowledge of the problem to be solved is added in the cost functional to obtain better generalization performance, and a simple and efficient method named cost functional minimized extreme and incremental learning (CFM-EIL) algorithm is proposed. In CFM-EIL based SLRRN (CFM-EIL-SLRRN), the ridgelet hidden neurons together with their parameters are tuned incrementally and analytically; thus it can significantly reduce the computational complexity of gradient based or other iterative algorithms. Some simulation experiments about time-series forecasting are taken, and several commonly used regression ways are considered under the same condition to give a comparison result. The results show the superiority of the proposed CFM-EIL-SLRRN to its counterparts in forecasting. (C) 2011 Elsevier B.V. All rights reserved.