Analyses Of HAB Nonlinear Dynamical Model And Fuzzy Neural Network Forcasting Research

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.