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Fast Learning Network with Parallel Layer Perceptrons

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Abstract

This paper proposes a novel artificial neural network called Parallel Layer Perceptron Fast Learning Network (PLP-FLN). In PLP-FLN, a parallel single hidden layer feed-forward neural network is added on the basis of Fast Learning Network (FLN) which is an improved Extreme Learning Machine (ELM). Input weights and hidden layer biases are randomly generated. The weights connect the output nodes and the input nodes, and the weights connect the output nodes and the hidden nodes are analytically determined based on least squares methods. In order to test the PLP-FLN validity, this paper compared it with ELM, FLN, Kernel ELM and Incremental ELM through 12 regression applications and 7 classification problems. By comparing the experimental results, it shows that the PLP-FLN with much more compact networks have demonstrated better approximations, classification performances and generalization ability.

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Acknowledgements

Project supported by the National Natural Science Foundation of China (Grant No. 61403331,61573306), Program for the Top Young Talents of Higher Learning Institutions of Hebei (Grant No. BJ2017033), Natural Science Foundation of Hebei Province (Grant No. F2016203427), China Postdoctoral Science Foundation (Grant No. 2015M571280), the Doctorial Foundation of Yanshan University (Grant No.B847), the natural science foundation for young scientist of Hebei province (Grant No.F2014203099) and the independent research program for young teachers of yanshan university (Grant No. 13LG006).

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Authors and Affiliations

  1. Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao, 066004, China

    Guoqiang Li, Xiaobin Qi, Bin Chen, Yunpeng Ma, Peifeng Niu & Zhiwang Chen

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  1. Guoqiang Li
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  2. Xiaobin Qi
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  3. Bin Chen
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  4. Yunpeng Ma
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  5. Peifeng Niu
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  6. Zhiwang Chen
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Correspondence to Xiaobin Qi.

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Li, G., Qi, X., Chen, B. et al. Fast Learning Network with Parallel Layer Perceptrons. Neural Process Lett 47, 549–564 (2018). https://doi.org/10.1007/s11063-017-9667-6

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  • DOI: https://doi.org/10.1007/s11063-017-9667-6

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