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Dendrite Net with Acceleration Module for Faster Nonlinear Mapping and System Identification

Author

Listed:
  • Gang Liu

    (School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
    Institute of Robotics and Intelligent Systems, Xi’an Jiaotong University, Xi’an 710049, China)

  • Yajing Pang

    (School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China)

  • Shuai Yin

    (Institute of Robotics and Intelligent Systems, Xi’an Jiaotong University, Xi’an 710049, China)

  • Xiaoke Niu

    (School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
    Henan Provincial Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou 450001, China)

  • Jing Wang

    (Institute of Robotics and Intelligent Systems, Xi’an Jiaotong University, Xi’an 710049, China)

  • Hong Wan

    (School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
    Henan Provincial Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou 450001, China)

Abstract

Nonlinear mapping is an essential and common demand in online systems, such as sensor systems and mobile phones. Accelerating nonlinear mapping will directly speed up online systems. Previously the authors of this paper proposed a Dendrite Net (DD) with enormously lower time complexity than the existing nonlinear mapping algorithms; however, there still are redundant calculations in DD. This paper presents a DD with an acceleration module (AC) to accelerate nonlinear mapping further. We conduct three experiments to verify whether DD with AC has lower time complexity while retaining DD’s nonlinear mapping properties and system identification properties: The first experiment is the precision and identification of unary nonlinear mapping, reflecting the calculation performance using DD with AC for basic functions in online systems. The second experiment is the mapping precision and identification of the multi-input nonlinear system, reflecting the performance for designing online systems via DD with AC. Finally, this paper compares the time complexity of DD and DD with AC and analyzes the theoretical reasons through repeated experiments. Results: DD with AC retains DD’s excellent mapping and identification properties and has lower time complexity. Significance: DD with AC can be used for most engineering systems, such as sensor systems, and will speed up computation in these online systems.

Suggested Citation

  • Gang Liu & Yajing Pang & Shuai Yin & Xiaoke Niu & Jing Wang & Hong Wan, 2022. "Dendrite Net with Acceleration Module for Faster Nonlinear Mapping and System Identification," Mathematics, MDPI, vol. 10(23), pages 1-14, November.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:23:p:4477-:d:985551
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    References listed on IDEAS

    as
    1. Han, Yongming & Li, Jingze & Lou, Xiaoyi & Fan, Chenyu & Geng, Zhiqiang, 2022. "Energy saving of buildings for reducing carbon dioxide emissions using novel dendrite net integrated adaptive mean square gradient," Applied Energy, Elsevier, vol. 309(C).
    2. Shousong Jin & Yanxi Chen & Yiping Shao & Yaliang Wang, 2022. "An Accuracy Prediction Method of the RV Reducer to Be Assembled Considering Dendritic Weighting Function," Energies, MDPI, vol. 15(19), pages 1-13, September.
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