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Self-Adaptive Evolutionary Extreme Learning Machine

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Abstract

In this paper, we propose an improved learning algorithm named self-adaptive evolutionary extreme learning machine (SaE-ELM) for single hidden layer feedforward networks (SLFNs). In SaE-ELM, the network hidden node parameters are optimized by the self-adaptive differential evolution algorithm, whose trial vector generation strategies and their associated control parameters are self-adapted in a strategy pool by learning from their previous experiences in generating promising solutions, and the network output weights are calculated using the Moore–Penrose generalized inverse. SaE-ELM outperforms the evolutionary extreme learning machine (E-ELM) and the different evolutionary Levenberg–Marquardt method in general as it could self-adaptively determine the suitable control parameters and generation strategies involved in DE. Simulations have shown that SaE-ELM not only performs better than E-ELM with several manually choosing generation strategies and control parameters but also obtains better generalization performances than several related methods.

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Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 639798, Singapore

    Jiuwen Cao, Zhiping Lin & Guang-Bin Huang

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  1. Jiuwen Cao
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  2. Zhiping Lin
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  3. Guang-Bin Huang
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Correspondence to Jiuwen Cao.

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Cao, J., Lin, Z. & Huang, GB. Self-Adaptive Evolutionary Extreme Learning Machine. Neural Process Lett 36, 285–305 (2012). https://doi.org/10.1007/s11063-012-9236-y

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