【作者】 葛根；

【导师】 王洪礼；

【作者基本信息】 天津大学 ， 工程力学， 2006， 硕士

【摘要】 随着沿海经济的高速发展,海洋富营养化程度日趋严重,这使赤潮的发生频率,规模和造成的危害都在不断加大.因此对赤潮藻类生长过程的研究成为国内外研究的热门话题.本文运用现代非线性动力学理论和模糊神经网络技术对赤潮藻类进行了研究,取得如下成果:首先,建立了新的赤潮藻类生态非线性动力学模型,该模型考虑了藻类,营养盐,还有浮游动物之间的摄食关系.研究了模型的平衡点的稳定性,以及HOPF分岔现象.为了更好的模拟出藻类周期性爆发生长,以及每次爆发的不规律性.提出了变参数的藻类生长模型,用一个周期阶跃函数模拟藻类的生长率随季节变化的规律.分析和数值模拟结果与实际吻合良好.其次在提出采用模糊神经网络方法预测赤潮藻类浓度.模糊神经网络具有神经网络和模糊推理的优点,避免了各自的缺点.一方面,它利用模糊系统的解释推理能力加强了神经网络对模型的解释能力,又利用神经网络的自学习功能克服了模糊技术中的对专家意见的依赖性和模糊集合的非自适应性.本文构建了一个四层的模糊神经网络模型,并且把结果和普通的BP神经网络和RBF神经网络进行比较,得出的赤潮藻类浓度模糊神经网络的预测结果相对更精准.本文还考虑水流动力学作用和光照等因素,提出了耦合流体动力学的赤潮藻类光照生长动力学模型.通过选用精确的差分格式,对模型做了数值模拟.得出的结论和实际的赤潮发生情况吻合良好.本论文得到国家自然基金项目的资助更多还原

【Abstract】 First, A simple nutrient phytoplankton nonlinear dynamical model was built to help understand the dynamics of algae blooms. The author studied the model to analyze the mechanism of the bloom recurrence. With one parameter changing continually, a saddle-node bifurcation was observed. In order to explain the seasonal blooms of phytoplankton, the modulation of phytoplankton growth this model was changed by a variable parameter approximated by periodical step-function with maxima at high season and minima at low season.. Some numerical simulation was done to gain the nutrients and phytoplankton time series and trajectory in phase space. The results show that this model fit the real condition better.Secondly, one four-layer fuzzy neural network using Back Propagation algorithm and fuzzy logical was built to study the nonlinear relationships between different physical -chemical factors and the denseness of red tide algae, and to anticipate the denseness of the red tide algae. For the first time, the fuzzy neural network technology was applied to research the prediction of red tide. Compared with BP network and RBF network, the outcome of this method is better.Thirdly, taking the hydrodynamics and the light into consider,a new red tide aglae growth dynamical model conbined with hydrodynamics was built. And numerical simulation was done with high oder discretisation,the results show the model can explain the real condition well.更多还原