Fitting transducer characteristics to measured data using a virtual curve tracer

Sensors and Actuators, A: Physical

Volumn 111, Issue 2-3, 2004, Pages 145-153

  Singh, Amar Partap ^a   Kumar, Shakti ^b   Kamal, Tara Singh ^a  

^a SANT LONGOWAL INSTITUTE OF ENGINEERING AND TECHNOLOGY   (India)

^b Haryana Engineering College   (India)

Author keywords

Calibration; Modeling; Nonlinearity; Transducer

Indexed keywords

APPROXIMATION THEORY; COMPUTER GRAPHICS; COMPUTER PROGRAMMING LANGUAGES; COMPUTER SOFTWARE; DATA ACQUISITION; FEEDFORWARD NEURAL NETWORKS; LEARNING ALGORITHMS; MICROPROCESSOR CHIPS; MULTILAYER NEURAL NETWORKS; NONLINEAR CONTROL SYSTEMS; PIECEWISE LINEAR TECHNIQUES; PROCESS CONTROL; STRAIN GAGES; TRANSFER FUNCTIONS;

DATA ACQUISITION SYSTEMS (DAS); VIRTUAL CURVE TRACER;

PRESSURE TRANSDUCERS;

EID: 1242343673     PISSN: 09244247     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.sna.2003.10.060     Document Type: Article

References (21)

1. J.M. Dias Pereira, P.M.B. Silva Girao, O. Postolache, Fitting transducer characteristics to measured data, IEEE Instrum. Meas. Mag. ( Dec 2001) 26-39.
2. Bolk W.T. A general digital linearizing method of transducers. J. Phys. E. 18:1985;61-64.
3. Patranabis D., Ghosh S., Bakshi C. Linearizing transducer characteristics. IEEE Trans. Inst. Meas. 37(1):1998;66-69.
4. J.P. Bentley, Principles of Measurement Systems, third ed., Pearson Education Asia Pvt. Ltd., Delhi, 2000.
5. Capital Equipment Test-point Software (Version 3.3) User's Manual, 1997.
6. B. Yegnanarayana, Artificial Neural Networks, PHI, New Delhi, 2001.
7. B. Kosko, Neural Networks and Fuzzy Systems, PHI, New Delhi, 2000.
8. Widrow B., Lehr M.H. 30 years of adaptive neural networks: perceptions, madaline and back-propagation. Proc. IEEE. 78:1990;1415-1442.
9. Dias Pereira J.M., Postolache O., Cretu M. Minimizing temperature drift errors of conditioning circuits using artificial neural networks. IEEE Trans. Inst. Meas. 49(5):2000;1122-1127.
10. O. Postolache, P. Girao, M. Pereira, Neural networks in automated measurement systems: state-of-the-art and new research trends, in: Proceedings of IEEE International Joint Conference on Neural Networks-IJCNN'01, vol. 3, Washington, DC, USA, 15-19 July 2001, pp. 2310-2315.
11. DARPA, Neural Network Study, MIT Lin-coln Laboratory, Lexington, MA, 1988 (Chapter 1).
12. Linko S., Zhu Y., Linko P. Applying neural networks as software sensors for enzyme engineering. Tibtech. 17:1999;155-163.
13. S. Haykin, Neural Networks: A Comprehensive Foundation, Pearson Education Asia, New Delhi, 2001, pp. 118-120.
14. J.C. Patra, R.N. Pal, Inverse modeling of pressure sensors using artificial neural networks, in: Proceedings of the AMSE International Conference Signals, Data and Systems, Banglore, India, 1993, pp. 225-236.
15. Patra J.C., Panda G., Baliarsingh R. Artificial neural network-based nonlinearity estimation of pressure sensors. IEEE Trans. Inst. Meas. 43:1994;874-881.
16. Pao Y.H., Philips S.M., Sobaje D.J. Neural net computing and intelligent control systems. Int. J. Control. 56(2):1992;263-289.
17. Patra J.C., Pal R.N., Chatterji B.N., Panda G. Identification of nonlinear dynamic systems using functional link artificial neural network. Cybernet., Part-B. 29(2):1999;254-262.
18. Patra J.C., Pal R.N., Chatterji B.N., Panda G. Nonlinear channel equalization for QAM signal cancellation using artificial neural network. IEEE Trans. Syst. Man Cybernat., Part-B. 29(2):1999;262-271.
19. L.M. Fu, Neural Networks in Computer Intelligence, Tata McGraw-Hill Publishing Company Limited, New Delhi, 2003.
20. Specht D.F. Probabilistic neural networks and polynomial adaline as complementary techniques for classification. IEEE Trans. Neural Networks. 1(1):1990;111-121.
21. Hagan M.T., Menhaj M. Training feed-forward networks with the Marquardt algorithm. IEEE Trans. Neural Networks. 5(6):1994;989-993.