Innovative NARX recurrent neural network model for ultra-thin shape memory alloy wire

Neurocomputing

Volumn 134, Issue , 2014, Pages 289-295

  Wang, Han ^a   Song, Gangbing ^a  

^a UNIVERSITY OF HOUSTON   (United States)

Author keywords

Elman network; Jordan network; NARX; RNN; SMA wire

Indexed keywords

HYSTERESIS; RECURRENT NEURAL NETWORKS; SHAPE MEMORY EFFECT;

ELMAN NETWORK; NARX; NARX NEURAL NETWORK; NONLINEAR AUTOREGRESSIVE MODEL WITH EXOGENOUS INPUT (NARX); RECURRENT NEURAL NETWORK MODEL; RNN; SHAPE MEMORY ALLOY WIRE; SMA WIRE;

WIRE;

ALLOY; SHAPE MEMORY ALLOY; UNCLASSIFIED DRUG;

ACCURACY; ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; CALCULATION; COMPARATIVE STUDY; JORDAN ELMAN NETWORK; MATHEMATICAL ANALYSIS; MATHEMATICAL MODEL; NONLINEAR AUTOREGRESSIVE MODEL WITH EXOGENOUS INPUT; PREDICTION; PRIORITY JOURNAL; SIMULATION;

EID: 84896544741     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2013.09.050     Document Type: Article

References (27)

1. Torra V., Isalgue A., Martorell F., Terriault P., Lovey F.C. Built in dampers for family homes via SMA: an ANSYS computation scheme based on mesoscopic and microscopic experimental analyses. Eng. Struct. 2007, 29(8):1889-1902.
2. de Araujo C.J., da Silva N.J., da Silva M.M., Gonzalez C.H. A comparative study of Ni-Ti and Ni-Ti-Cu shape memory alloy processed by plasma melting and injection molding. Mater. Des. 2011, 32(10):4925-4930.
3. Kuribayashi K., Tsuchiya K., You Z., Tomus D., Umemoto M., Ito T., Sasaki M. Self-deployable origami stent grafts as a biomedical application of Ni-rich TiNi shape memory alloy foil. Mater. Sci. Eng.: A 2006, 419(1-2):131-137.
4. Chau E.T., Friend C.M., Allen D.M., Hora J., Webster J.R. A technical and economic appraisal of shape memory alloys for aerospace applications. Mater. Sci. Eng.: A 2006, 438-440:589-592.
5. Meier H., Oelschlaeger L. Numerical thermomechanical modelling of shape memory alloy wires. Mater. Sci. Eng. A 2004, 378:484-489.
6. Shaw J.A. A thermomechanical model for a 1-D shape memory alloy wire with propagating instabilities. Int. J. Solids Struct. 2002, 39:1275-1305.
7. Mayergoyz I. Mathematical Models of Hysteresis and their Applications 2003, Elsevier Science Inc.
8. Smith R.C. Smart Material Systems: Model Development 2005, SIAM Press, Philadilphia, PA.
9. Tanaka K., Kobayashi S., Sato Y. Thermomechanics of transformation pseudoelasticity and shape memory effects in alloys. Int. J. Plasticity 1986, 2:59-72.
10. Siredey N., Patoor E., Berveiller M., Eberhardt A. Constitutive equations for polycrystalline thermoelastic shape memory alloys, part I. Intragranular interactions and behavior of the grain. Int. J. Solids Struct. 1999, 36:4289-4315.
11. P.L. Potapov, E.P. da Silva. Actuator 2000, in: Proceedings of the 7th International Conference on New Actuators. Bremen, Germany, 2000, pp. 156-162.
12. Zhang X., Sun Q.P., Yu S. A non-invariant plane model for the interface in CuAlNi single crystal shape memory alloys. J. Mech. Phys. Solids 2000, 48:2163-2182.
13. Asua E., Etxebarria V., Garcia-Arribas A. Neural network-based micropositioning control of smart shape memory alloy actuators. Eng. Appl. Artif. Intell. 2008, 21:796-804.
14. Hornik K., Stinchcombe M., White H. Multilayer feedforward networks are universal approximators. Neural Networks 1989, 2(5):359-366.
15. Thawornwong S., Enke D. The adaptive selection of financial and economic viariables for use with artificial neural networks. Neurocomputing 2004, 56:205-232.
16. Lisboa P.J., Taktak A.F. The use of artificial neural networks in decision support in cancer: a systematic review. Neural Networks 2006, 19(4):408-415.
17. Ye H., Nicolai R., Reh L. A Bayesian-Gaussian neural network and its applications in process engineering. Chem. Eng. Process.: Process Intensif. 1998, 37(5):439-449.
18. Karras D.A., Zorkadis V. On neural network techniques in the secure management of communication systems through improving and quality assessing pseudorandom stream generators. Neural Networks 2003, 16(5-6):899-905.
19. Zhang X., Tan Y., Su M. Modeling of hysteresis in piezoelectric actuators using neural networks. Mech. Syst. Signal Process. 2009, 23(8):2699-2711.
20. Li Q., Yu J., Mu B., Sun X. BP neural network prediction of the mechanical properties of porous NiTi shape memory alloy prepared by thermal explosion reaction. Mater. Sci. Eng.: A 2006, 419(1-2):214-217.
21. Siegelmann H.T., Horne B.G., Giles C.L. Computational capabilities of recurrent NARX neural networks. IEEE Trans. Syst. Man Cybern. 1997, B-27(2):208-215.
22. Schafer A.M., Zimmermann H.G. Recurrent neural networks are universal approximators. Int. J. Neural Syst. 2007, 17(4):253-263.
23. Luo H., Puthusserypady S. Spatia-temporal modeling and analysis of fMRI data using NARX neural network. Int. J. Neural Syst. 2006, 16(2):139-149.
24. Puscasu G., Codres B., Stancu A., Murariu.G. Nonlinear system identification based on internal recurrent neural networks. Int. J. Neural Syst. 2009, 19(2):115-125.
25. Lien J.P., York A., Fang T., Buckner G.D. Modeling piezoelectric actuators with hysteretic recurrent neural networks. Sens. Actuators A: Phys. 2010, 163(2):516-525.
26. Levenberg K. A method for the solution of certain nonlinear problems in least squares. Q. Appl. Math. 1944, 2:164-168.
27. Marquardt D. An algorithm for least-squares estimation of nonlinear parameters. SIAM J. Appl. Math. 1963, 11(2):431-441.