模块小波神经网络在工业产品质量控制中的应用  

Application of modular wavelet neural networks in industries product quality control

作　　者：李换琴[1] 万百五[2] 

机构地区：[1]西安交通大学理学院,陕西西安710049 [2]西安交通大学系统工程研究所,陕西西安710049

出　　处：《控制与决策》2004年第3期295-298,共4页Control and Decision

基　　金：国家863计划资助项目(863-51-011);国家自然科学基金资助项目(60274055);西安交通大学自然科学基金资助项目(0900-5-73024).

摘　　要：针对输入空间包含多种类型的数据时,以单一的神经网络为模型,其收敛很困难的问题,提出一种基于模块小波神经网络的建模方法.利用分而治之思想,模块神经网络通过一个门控网络进行分类和协调,可以将一个复杂任务分解成几个简单的子任务,每个子任务由一个局部专家网络学习.与传统的模块网络不同,这里的专家网络是小波网络而不是BP网络.将所提出的网络模型用于热连轧产品质量建模,并与单一的神经网络建模结果进行比较.建模结果表明,模块小波神经网络模型优于单一神经网络模型.When the input space consists of several different classes of input data, it becomes very difficult to converge the network during the training phase. A modular wavelet neural-network is presented to overcome this difficulty. Based on the divide-and-conquer concept, a modular network is capable of dividing a complex task into subtasks, and modeling each subtasks with an expert network. To model such activities, a gating network is used for the classification and allocation of the input data to the corresponding expert network. Different from traditional modular networks, here each expert network is a wavelet network. The performance of such networks in modeling product quality is examined and compared with that of singular networks. Modeling results demonstrate that the proposed method takes a significant improvement over the general singular network model.

关 键 词：模块小波网络 高维输入 质量模型 热连轧机 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]