The extreme learning machine learning algorithm with tunable activation function |
| |
Authors: | Bin Li Yibin Li Xuewen Rong |
| |
Affiliation: | 1. School of Control Science and Engineering, Shandong University, Jinan, 250061, People’s Republic of China 2. School of Science, Shandong Polytechnic University, Jinan, 250353, People’s Republic of China
|
| |
Abstract: | In this paper, we propose an extreme learning machine (ELM) with tunable activation function (TAF-ELM) learning algorithm, which determines its activation functions dynamically by means of the differential evolution algorithm based on the input data. The main objective is to overcome the problem dependence of fixed slop of the activation function in ELM. We mainly considered the issue of processing of benchmark problems on function approximation and pattern classification. Compared with ELM and E-ELM learning algorithms with the same network size or compact network configuration, the proposed algorithm has improved generalization performance with good accuracy. In addition, the proposed algorithm also has very good performance in the TAF neural networks learning algorithms. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|