Modelling Hysteresis in Shape Memory Alloys Using LSTM Recurrent Neural Network

Modelling Hysteresis in Shape Memory Alloys Using LSTM Recurrent Neural Network

The complex behavior of shape memory alloys (SMAs), characterized by hysteresis and nonlinear dynamics, results in complex constitutive equations. To circumvent the complexity of solving these equations, a black box neural network (NN) has been employed in this research to model a rotary actuator ac...

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Bibliographic Details
Main Authors:	Mohammad Reza Zakerzadeh, Seyedkeivan Naseri, Payam Naseri
Format:	Article
Language:	English
Published:	Hindawi Limited 2024-01-01
Series:	Journal of Applied Mathematics
Online Access:	http://dx.doi.org/10.1155/2024/1174438

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