无师训练Fuzzy Min-Max人工神经网络  

A Unsuper vised Fuzzy Min-Max Artificial Neural Network

作　　者：张青贵[1] 杨露菁[1] 王昕晔 

机构地区：[1]海军电子工程学院二系,南京211800

出　　处：《系统工程与电子技术》1999年第11期83-86,共4页Systems Engineering and Electronics

摘　　要：提出了一种无师训练的ｆｕｚｚｙ ｍ ｉｎｍ ａｘ 人工神经网络，它兼有一般ｆｕｚｚｙ ｍ ｉｎｍ ａｘ 网与ＡＲＴ２网的优点，既弥补了ｆｕｚｚｙ ｍ ｉｎｍ ａｘ 网不能自适应在线学习新类的缺陷，又消除了ＡＲＴ２网警戒门限过高的弊病。经模式识别仿真对比，对同样的输入数据，我们提出的网络用较低的警戒门限值即可达到ＡＲＴ２用很高的警戒门限值才能达到的分类效果，且计算量大大减少。对模式识别而言，所提出的网络比ｆｕｚｚｙ ｍ ｉｎｍ ａｘ 网和ＡＲＴ２网更具有实用价值。A unsupervised fuzzy m in m ax artificialneuralnetwork is proposed in this paper. Our network pos sesses the strong points both of the traditionalfuzzy m in m ax net and the ART2 net. It not only counteracts the weakness which m akes the traditionalfuzzy m in m ax netto be incapable oflearning from any new pattern class, but also overcom es the shortcom ing w hich causes using too high vigilance threshold in ART2. The resultofsim ulating pattern recognition show sthatournetcan achieve abettereffectofpattern recognition w ith lowerthreshold than that ART2 does w ith higherthreshold using the sam e data. The com putationalcom plexity ofournetislowerthan thatof ART2. Ourconclusion isthatournetpossessesm ore practicalvaluethan thatoffuzzy m in m ax netand ART2 netfor pattern recognition.

关 键 词：目标识别 拓扑网络 门限控制 人工神经网络 

分 类 号：TN959.17[电子电信—信号与信息处理] TP18[电子电信—信息与通信工程]