基于粗集约简的Fuzzy ART设备状态监测方法  

Monitoring Method of Fuzzy ART Equipment Condition Based on Rough Set Attribute Reduction

作　　者：栾凤艳[1] 谷吉海[2] 巴兴强[3] 

机构地区：[1]黑龙江林业职业技术学院,牡丹江157011 [2]哈尔滨商业大学 [3]东北林业大学

出　　处：《东北林业大学学报》2007年第6期99-101,共3页Journal of Northeast Forestry University

基　　金：黑龙江省教育厅科学技术研究项目(11511099);黑龙江省自然科学基金项目(E200615)

摘　　要：针对复杂设备系统的多工况状态监测问题,提出一种基于粗集属性约简的FuzzyART神经网络状态监测方法。该方法利用粗集的信息决策表和决策矩阵对系统的监测参数进行约简提取,降低FuzzyART输入向量的维数。实例测试结果表明,采用粗集约简提取的监测向量与原监测向量具有相同的监测能力,且提高了网络的学习效率。A new monitoring method based on Fuzzy ART neural network of rough set reduction is presented aiming at the existing problems in state monitoring for a complex system under multiple loading eases. The method uses rough information deeision-making form and decision-making matrix to choose monitoring parameters for the system, thus can effectively reduce the dimension of Fuzzy ART input vectors. The test result proves that the monitoring vector selected by rough reduction has the same monitoring capability with the original monitoring vector; therefore, network efficiency is improved.

关 键 词：粗糙集 FUZZY ART 复杂设备 状态监测 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]