Evolutionary Circular Extreme Learning Machine

Abstract

Circular Extreme Learning Machine (C-ELM) is an extension of Extreme Learning Machine. Its power is mapping both linear and circular separation boundaries. However, C-ELM uses the random determination of the input weights and hidden biases, which may lead to local optimal. This paper proposes a hybrid learning algorithms based on the C-ELM and the Differential Evolution (DE) to select appropriate weights and hidden biases. It called Evolutionary circular extreme learning machine (EC-ELM). From experimental results show EC-ELM can slightly improve C-ELM and also reduce the number of nodes network.