人工神经网络在单级连续中和过程辨识的应用  

Application of Artificial Neural Network for te pH process Identification in Single Loop Neutralizer System

作　　者：周玉宏[1] 王俊伟[2] 

机构地区：[1]公安海警高等专科学校基础部,浙江宁波315801 [2]杭州电子科技大学自动化学院,浙江杭州310018

出　　处：《杭州电子科技大学学报（自然科学版）》2008年第3期76-80,共5页Journal of Hangzhou Dianzi University：Natural Sciences

摘　　要：单级连续中和器pH中和过程具有严重非线性和不确定性,难以建立精确模型,这给精确控制带来难题。该文利用人工神经网络对非线性特性的自学习、自适应能力,为pH中和过程的辨识提供了新的途径,并进行了相关试验和仿真研究。结果表明,神经网络在单级连续中和器pH中和过程辨识中具有较好的精度和动态响应。The pH neutralization process in the single loop neutralizer system is extraordinary nonlinear and uncertain, it＇ s difficult to be accurately modeled. In this paper, we use the selfleaming and selfadaptive BP neutral network to establish a typical pH process model and test it. The results proved that this identification method is effective and the neutral network has a broad prospect in the field of pH process identification in the single loop neutralizer system.

关 键 词：酸碱中和过程 辨识 神经网络 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]