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A neural model of hysteresis in amorphous materials and piezoelectric materials

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Abstract

A new approach to constructing hysteretic operator (HO) is proposed in this paper. Based on the HO, the input space of neural networks is expanded from one-dimension to two-dimension and the multi-value mapping of hysteresis is transformed into a continuous mapping comprised of one-to-one mapping and multiple-to-one mapping. Based on the expanded input space, a neural network is employed to approximate hysteresis. The results of experimental examples suggest the proposed approach is effective.

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Acknowledgments

This work is partially supported by the Zhejiang Provincial Natural Science Foundation (Grant No. Y1110508), the Interdisciplinary Pre-research Project of Zhejiang University of Science & Technology (Grant No. 2011JC03Y), Science Technology Department of Zhejiang Province (Grant No. 2014C31020) and National Natural Science Foundation of China (Grant No. 11304282).

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Authors and Affiliations

  1. School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou, 310023, China

    Lianwei Ma

  2. Department of Applied Physics, Zhejiang University of Science and Technology, Hangzhou, 310023, China

    Yu Shen

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  1. Lianwei Ma
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  2. Yu Shen
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Correspondence to Lianwei Ma.

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Ma, L., Shen, Y. A neural model of hysteresis in amorphous materials and piezoelectric materials. Appl. Phys. A 116, 715–722 (2014). https://doi.org/10.1007/s00339-014-8528-7

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  • DOI: https://doi.org/10.1007/s00339-014-8528-7

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