期刊文献+

生态学数学模型参数优化估计的遗传算法 被引量:2

THE APPLICATION OF GENETIC ALGORITHM IN DISCRIMINATION OF NONLINEAR MODELS IN ECOLOGY
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摘要 本文提出用遗传算法,对生态学中的一些数学模型参数进行优化估计,并以崔-lawson方程为例.尝试了遗传算法的效果.结果表明,该方法性能良好,可望成为生态学中各类非线性模型辨识的有效参数. Genetic algorithm is used to discriminate nonlinear models in ecology and test its effectiveness by selecting a set of samples with the example of an expotential curve,which shows that it operates well and is expected to become an effective tool to discrimination of various nonlinear models in ecology.
出处 《生物数学学报》 CSCD 北大核心 1995年第4期61-65,共5页 Journal of Biomathematics
关键词 崔-lawson方程 遗传算法 参数估计 Ecology,Nonlinear Models Disrimination,Genetic Algorithm
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参考文献1

  • 1Kenneth Jong. Learning with genetic algorithms: An overview[J] 1988,Machine Learning(2-3):121~138

同被引文献12

  • 1蔡煜东,姚林声,陈德辉,汪礼祁,洪伟.用人工神经网络方法估计Ligistic方程参数[J].生物数学学报,1994,9(1):28-31. 被引量:8
  • 2赵甘霖.昆虫种群空间分布型程序[J].计算机农业应用,1995(2):21-25. 被引量:2
  • 3王福林,王吉权,吴昌友,吴秋峰.实数遗传算法的改进研究[J].生物数学学报,2006,21(1):153-158. 被引量:30
  • 4李典谟.介绍几种昆虫分布型理论公式的计算[J].昆虫知识,1965,9(5):201-205.
  • 5Draper N R, Smith H. Applied Nonlinear Regmssion[M]. 3rd edition. New York: John Wiley and Sons Inc, 1998, 505-566.
  • 6Holland J H. Adaptation in Natural and Artificial Systems[M] . Ann Arbor: The University of Michigan Press,1975, 89-120.
  • 7Sivanandam S N, Deepa S N. Introduction to Genetic Algorith, ms[M]. New York: Springer Berlin Ileidelberg,2008, 33-36.
  • 8Grosan C, Abraham A, Ishibuchi H. Hybrid Evolutionary Algorithms[M]. New York: Springer Berlin Heidelberg, 2007.
  • 9Katare S, Bhan A, James M, et al. A hybrid genetic algorithm for efficient parameter estimation of large kinetic models [J]. Computers & Chemical Engineering, 2004, 28(12): 2569-2581.
  • 10Xiao J. Hybrid Genetic Algorithm: A Robust Parameter Estimation Technique and its Application to Heavy Duty Vehicles [J]. Journal of Dynamic Systems, Measurement, and Control, 2006, 128(3):523-532.

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