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The isolation layered optimization algorithm of MIMO polygonal fuzzy neural network

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Abstract

The single-input single-output or multi-input single-output polygonal fuzzy neural network can accomplish some information disposing based on a finite number of points of polygonal fuzzy number. Although it does not depend on a precise mathematical model, it involves logical reasoning, numerical calculation and nonlinear functional approximation. The multi-input multi-output (MIMO) polygonal fuzzy neural network model is proposed for the first time in this article. The two different algorithms are designed in the input layer and hidden layer, respectively, and some parameters of the connection weights in the isolation layered manner can be optimized. Particularly, the neurons in the hidden layer are optimized one by one. Results showed that the isolation layered optimization algorithm of MIMO polygonal fuzzy neural network could improve the computational efficiency and convergent rate.

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Acknowledgment

This work has been supported by National Natural Science Foundation China (Grant No. 61374009).

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Authors and Affiliations

  1. School of Mathematics Science, Tianjin Normal University, Tianjin, 300387, China

    Guijun Wang

  2. School of Mathematics, Tonghua Normal University, Tonghua, 134002, China

    Chunfeng Suo

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  1. Guijun Wang
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  2. Chunfeng Suo
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Correspondence to Guijun Wang.

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Wang, G., Suo, C. The isolation layered optimization algorithm of MIMO polygonal fuzzy neural network. Neural Comput & Applic 29, 721–731 (2018). https://doi.org/10.1007/s00521-016-2600-5

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  • DOI: https://doi.org/10.1007/s00521-016-2600-5

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