Time-delayed dynamic neural network-based model for hysteresis behavior of shape-memory alloys. Issue 6 (August 2016)

• Record Type: Journal Article
• Authors: Mai, Huanhuan; Song, Gangbing; Liao, Xiaofeng
• Is Part Of: Neural computing & applications. Volume 27:Issue 6(2016)
• Page Start: 1519
• Page End: 1531
• Publication Date: 2016-08
• Subjects: Time-delayed dynamic neural network -- Hysteresis -- Identification -- Shape-memory alloys; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-1950-8
• Languages: English
• ISSNs: 0941-0643

Abstract

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.