[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(
)
基于遗传算法的非线性传感器模型辨识
)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
- 基于遗传算法的非线性传感器模型辨识
- 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.
- 提出了一种新的、基于遗传算法的非线性传感器逆模型建模方法.利用遗传算法建模, 可以方便、准确地辨识未知非线性模型的系数.仿真实验表明该方法较传统方法, 具有更好的灵活性与适应性, 可以方便地实现在线修正模型系数, 因此提高了传感器的测量精度.由于遗传算法可以实现模型系数空间的全局搜索, 因此可以避免在模型系数训练过程中陷入局部极小点.
References:
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[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.