Extreme Learning Machine with Fuzzy Activation Function
Hieu Trung Huynh, Yonggwan Won · 2009
Recently, an efficient learning algorithm called extreme learning machine (ELM) has been proposed for single-hidden layer feed forward neural networks (SLFNs). Unlike the traditional gradient-descent based learning algorithms which determine network weights by iterative processes, the ELM algorithm analytically determines the output weights with random choice of input weights and hidden layer biases. This algorithm can achieve good performance with very high learning speed. In this paper, we propose a novel fuzzy-based activation function for SLFNs trained by ELM algorithm. This is a simple sigmoid-like nonlinear activation function and more suitable for hardware implementation. The experimental results for real applications show that this activation function offers good performance which is compatible to the sigmoidal activation function.