将人工神经网络（ANN）技术应用于饮用水处理厂混凝剂投加量的控制。混凝剂投加速率与原水参数（如浊度、电导率、pH值、温度等）呈非线性相关。应用的一个重要要求是系统对错误传感器测量或异常水特性的鲁棒性。开发的混合系统包括基于Kohonen自组织特征图的原始数据验证和重建，以及使用多层感知预测混凝剂用量。该系统的一个关键特点是能够考虑各种不确定性来源，例如非典型输入数据、测量误差和训练集的有限信息内容。给出了实际数据的实验结果。包括9个参考文献、图表。

Artificial Neural Network (ANN) techniques are applied to the control of coagulant dosing in a drinking water treatment plant. Coagulant dosing rate is non-linearly correlated to raw water parameters such as turbidity, conductivity, pH, temperature, etc. An important requirement of the application is robustness of the system against erroneous sensor measurements or unusual water characteristics. The hybrid system developed includes raw data validation and reconstruction based on a Kohonen self-organizing feature map, and prediction of coagulant dosage using multilayer perceptions. A key feature of the system is its ability to take into account various sources of uncertainty, such as atypical input data, measurement errors and limited information content of the training set. Experimental results with real data are presented. Includes 9 references, figures.