A new learning algorithm for function approximation incorporating a priori information into extreme learning machine

Abstract

In this paper, a new algorithm for function approximation is proposed to obtain better generalization performance and faster convergent rate. The new algorithm incorporates the architectural constraints from a priori information of the function approximation problem into Extreme Learning Machine. On one hand, according to Taylor theorem, the activation functions of the hidden neurons in this algorithm are polynomial functions. On the other hand, Extreme Learning Machine is adopted which analytically determines the output weights of single-hidden layer FNN. In theory, the new algorithm tends to provide the best generalization at extremely fast learning speed. Finally, several experimental results are given to verify the efficiency and effectiveness of our proposed learning algorithm. © Springer-Verlag Berlin Heidelberg 2006.