Symmetric extreme learning machine |
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Authors: | Xueyi Liu Ping Li Chuanhou Gao |
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Affiliation: | 1. School of Aeronautics and Astronautics, Zhejiang University, Hangzhou, 310027, Zhejiang, China 2. Department of Mathematics, Zhejiang University, Hangzhou, 310027, China
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Abstract: | Extreme learning machine (ELM) can be considered as a black-box modeling approach that seeks a model representation extracted from the training data. In this paper, a modified ELM algorithm, called symmetric ELM (S-ELM), is proposed by incorporating a priori information of symmetry. S-ELM is realized by transforming the original activation function of hidden neurons into a symmetric one with respect to the input variables of the samples. In theory, S-ELM can approximate N arbitrary distinct samples with zero error. Simulation results show that, in the applications where there exists the prior knowledge of symmetry, S-ELM can obtain better generalization performance, faster learning speed, and more compact network architecture. |
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