基于自适应共振理论(ART)网络的机床设备实例的分类  

Classifying of cases model by means of ART neural network in machine equipment

作　　者：陈桦[1] 程云艳[2] 陈静[2] 赵汝嘉[1] 

机构地区：[1]西安交通大学研究所,陕西西安710049 [2]陕西科技大学计算机与信息工程学院,陕西咸阳712081

出　　处：《组合机床与自动化加工技术》2004年第10期33-34,36,共3页Modular Machine Tool & Automatic Manufacturing Technique

摘　　要：阐述了自适应共振理论 (ART)网络的原理及训练算法。利用ART网络良好的自组织 ,自学习 ,可塑性强的特点 ,提出基于神经网络的机床设备实例分类的策略。该方法求解简单快速 ,容易实现 ,效率高 。This paper expatiates the principle and train algorithm of adaptive resonance theory. A new approach to the neural classifying model of machine equipment is presented on the base of its self-learning and self-orgnizing characteristics. The advantages of this model are the more effective classifying and the easier knowledge acquisition based on neural networks which has a bright application future.

关 键 词：ART1网络 分类 编码 自适应共振理论 机床设备 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]