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Time-delayed dynamic neural network-based model for hysteresis behavior of shape-memory alloys

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Abstract

Shape-memory alloys (SMAs) have received considerable amount of attentions for their engineering applications in recent years. The hysteresis in SMAs is sensitive to the state-varying tendency and frequency. Utilizing past information to estimate the hysteretic behavior gets increasing attention. In this paper, a time-delayed dynamic neural network (TDDNN) is proposed for modeling hysteresis of SMAs in online applications. By introducing a time delay between the input and output response, the TDDNN considers the time delay’s effect on the hysteresis. This proposed network was applied to a SMA wire actuator. Experimental results demonstrate the effectiveness of TDDNN. The identified results obtained by TDDNN are better than those obtained by dynamic neural network without considering the delay information. It demonstrates the importance of introducing the time delay. The different values of time delay item can also affect TDDNN’s identified results.

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Acknowledgments

The comments from Dr. Han Wang are greatly appreciated. We also appreciate the supports from Natural Science Foundation of Henan Province Education Office (No. 14A120001) and the supports from Henan University of Technology High level talent fund (No. 2013BS004).

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Correspondence to Huanhuan Mai.

Appendix

Appendix

For readers’ better understanding the manuscript, the differences between DNN and TDDNN are organized in the following Table 5.

Table 5 Summary of the differences between DNN and TDDNN

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Mai, H., Song, G. & Liao, X. Time-delayed dynamic neural network-based model for hysteresis behavior of shape-memory alloys. Neural Comput & Applic 27, 1519–1531 (2016). https://doi.org/10.1007/s00521-015-1950-8

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