摘要
简要介绍了遗传神经网络方法的原理 ,探讨了应用遗传神经网络方法研究辽东湾海域丹麦细柱藻 (Leptocylindrusdanicus)赤潮与其环境因子之间的关系 ,计算了各环境因子对丹麦细柱藻赤潮的贡献。结果表明 ,温度、盐度、DIN的变化对研究海域丹麦细柱藻种群密度的增长有比较重要的影响 ,DIN是营养限制因子。遗传神经网络是分析赤潮监测数据的有效方法 。
In this paper, the principles of genetic neural network method are introduced, its application in studying the relation of Leptocylindrus danicus red tide to environmental factors in the Liaodong Bay is explored, and the contributions of different environmental factors to Leptocylindrus danicus red tide are calculated. It is shown from the calculated results that the changes in salinity, temperature and dissolved inorganic nitrogen(DIN) play an important role in the increase of Leptocylindrus danicus population density in the study area, and DIN is a nutrient limiting factor. Genetic neural network method is an effective approach for analysing the red tide monitoring data and is worth exploring.
出处
《黄渤海海洋》
CSCD
北大核心
2002年第2期77-82,共6页
Journal of Oceanography of Huanghai & Bohai Seas
基金
国家"十五"攻关--海洋灾害预报及减灾技术 (0 5课题 )资助项目
关键词
遗传算法
BP网络
辽东湾
丹麦细柱藻
赤潮
genetic algorithm
back propagation network
Liaodong Bay
Lepto cylindrus danicus
red tide
