[1] Zhuang Zhemin*, Huang Weiyi,. Genetic Algorithm-Based Estimation of Nonlinear Transducer [J]. Journal of Southeast University (English Edition), 2001, 17 (1): 4-7. [doi:10.3969/j.issn.1003-7985.2001.01.002]

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Genetic Algorithm-Based Estimation of Nonlinear Transducer()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

17

Issue:

2001 1

Page:

4-7

Research Field:

Automation

Publishing date:

2001-06-30

Info

Title:

Genetic Algorithm-Based Estimation of Nonlinear Transducer

Author(s):

Zhuang Zhemin^*;  Huang Weiyi

Department of Instrument Science and Engineering, Southeast University, Nanjing 210096, China

Keywords:

nonlinear transducer;  genetic algorithm;  inverse model

PACS:

TP212

DOI:

10.3969/j.issn.1003-7985.2001.01.002

Abstract:

This paper describes an innovative, genetic algorithm-based inverse model of nonlinear transducer. In the inverse modeling, using a genetic algorithm, the unknown coefficients of the model are estimated accurately. The simulation results indicate that this technique provides greater flexibility and suitability than the existing methods. It is very easy to modify the nonlinear transducer on line. Thus the method improves the transducer’s accuracy. With the help of genetic algorithm(GA), the model coefficients’ training are less likely to be trapped in local minima than traditional gradient-based search algorithms.

References:

[1] S.K.Clakr, and K.D.Wise, Pressure sensitivity in anisotropically etched thin diaphragm pressure sensors, IEEE Trans.on Electron Devices, vol.26, no.12, pp.1887-1896, 1997
[2] M.Yamada, T.Takebayashi, S.I. Notoyama, and K.Watanabe, A switched capacitor interface for capacitive pressure sensors, IEEE Trans.on Instrum.Meas., vol.41, no.2, pp.81-86, 1992
[3] G.Betta, C.Liguori, and A.Pietrosanto, The use of genetic algorithms for advanced instrument fault detection and isolation schemes, In: Proc. of IEEE Instrumentation Measurement Technol. Conf., Brussels, Belgium, pp.1129-1134, June 1996
[4] D.E.Goldberg, Genetic algorithm in search, optimization and machine learning, Addison-Wesley, MA, 1989
[5] D.Park, A.Kandle, and G.Langholz, Genetic-based new fuzzy reasoning models with application to fuzzy control, IEEE Trans.on Syst., Man, Cybern., vol.24, no.1, pp.39-47, 1994

Memo

Memo:

* Born in 1965, male, graduate.

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