| 水处理过程的RBF和BP神经网络建模 |
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| 引用本文： | 沈捷,王莉,林锦国.水处理过程的RBF和BP神经网络建模[J].微计算机信息,2007,23(34):294-296. |
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| 作者姓名： | 沈捷 王莉 林锦国 |
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| 作者单位： | 南京工业大学自动化学院,江苏,210009 |
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| 基金项目： | 江苏省高校自然科学研究计划 |
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| 摘 要： | 针对水处理过程非线性、时变和大滞后的特点，本文采用RBF和BP神经网络分别建立了水处理过程模型，利用水厂实际运行数据对两个模型分别进行了训练和检验。与BP神经网络模型相比，RBF神经网络模型具有逼近能力强、收敛速度快等优点。该模型可以实现对水处理过程的在线辨识，并可进一步用于该过程的神经网络预测控制。
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| 关 键 词： | 水处理 神经网络 建模 |
| 文章编号： | 1008-0570（2007）12-1-0294-03 |
| 修稿时间： | 2007-09-13 |
RBFNN and BPNN Model of Water Treatment Process |
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| SHEN JIE,WANG LI,LIN JINGUO.RBFNN and BPNN Model of Water Treatment Process[J].Control & Automation,2007,23(34):294-296. |
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| Authors: | SHEN JIE WANG LI LIN JINGUO |
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| Affiliation: | 210009 College of Automation,Nanjing University of Technology |
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| Abstract: | Considering the nonlinear, time-varying and time-delaying property of water treatment process, this paper develops two models of water treatment separately based on RBF and BP neural network. The models have been trained and checked separately by practical data of water plant. Compared with the model based on BP neural network, the model based on RBF neural network has better features such as excellent approximation and fast converge speed. Online identification of water treatment process can be implemented by this model, which also can be applied in neural network predictive control. |
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| Keywords: | RBF BP |
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