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On state prediction for algae growth in seawater based on fuzzy back-propagation network

机译:基于模糊反向传播网络的海水藻类生长状态预测

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The state of algae reproduction is a key index for the status of seawater quality and pollutants emission for rivers. Algae growth is affected by many physical-chemical factors, this kind of complex relationship is difficult to be described by ordinary mechanism expression. Fuzzy back-propagation network can describe the complex nonlinear system, and it has a fine performance of generalization, it can give a dynamic estimate to the output variables of the system. We use PCA(Principal Component Analysis) method to reduce the dimension of the sample data, simplify the complexity of the model system, it can make the model has a fine convergence rate. The practical testing illustrates that fuzzy back-propagation network model based on PCA can be applied in state prediction for algae growth to good purpose.
机译:藻类繁殖状态是河流海水质量和污染物排放状况的关键指标。藻类的生长受许多物理化学因素的影响,这种复杂的关系很难用普通的机制表达来描述。模糊反向传播网络可以描述复杂的非线性系统,并且具有良好的泛化性能,可以对系统的输出变量进行动态估计。我们使用PCA(主成分分析)方法来减少样本数据的维数,简化模型系统的复杂性,可以使模型具有良好的收敛速度。实际测试表明,基于PCA的模糊反向传播网络模型可用于藻类生长的状态预测,以达到良好的目的。

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