Nonlinear Steady State Modeling for River Quality by a Neural Network with a Self-Selection Ability of Network Structure
Nonlinear Steady State Modeling for River Quality by a Neural Network with a Self-Selection Ability of Network Structure
复制标题
具有网络结构自选择能力的神经网络河流水质非线性稳态建模
DOI：
10.5687/iscie.10.277
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发表时间：
1997
期刊：
影响因子：
--
通讯作者：
T. Kondo
中科院分区：
文献类型：
--
作者：
T. Kondo
利用具有自选择网络结构能力的神经网络对河流水质进行了非线性稳态建模。该神经网络算法是对神经网络的一种修正算法，可以对结构非常复杂的非线性系统进行辨识。利用河流水质的BOD、DO浓度等实测数据，利用神经网络对河流水质的非线性稳态模型进行辨识，并与物理模型和GMDH模型的辨识结果进行了比较。结果表明，与物理模型和GMDH模型相比，本文的神经网络具有更好的预测效果。
This paper deals with a nonlinear steady state modeling of river quality by a neural network which has a self-selection ability of network structure. This neural network algorithm is a revised one of the neural network which can identify a nonlinear system whose structure is very large and complex. By using measured data of river quality such as BOD and DO concentrations, a nonlinear steady state model of river quality is identified by the neural network and the results are compared with the results which are obtained by a physical model and a GMDH model. And it is shown that the neural network in this paper gives better prediction results as compared with a physical model and a GMDH model.