📄 lvq模式分类网络的设计.htm
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<TD width=556> 您现在的位置： <A
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<TD class=title_maintxt vAlign=center width="82%"> <IMG
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align=absMiddle> LVQ模式分类网络的设计 </TD>
<TD class=title_maintxt width="18%"> <FONT
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<TD colSpan=2 height=40><FONT
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<TD colSpan=2>［
作者：cac 转贴自：本站原创 点击数：281 更新时间：2004-7-31 文章录入：<A
href="http://homepage.scau.edu.cn/club/lixue/01c4/UserInfo.asp?UserID=1">admin</A>
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<P>学习向量化网络由两层组成，第一层为竞争网络，第二层为线性层。竞争层能够学习对输入向量的分类，这与前面自组织竞争网络非常相识。线性层将竞争层传来的分类信息转变成使用者所定义的类别，。将经竞争层学习到的类称为子类，将现行层学习得到的类称为目标类。<BR><BR>%指定输入二维向量及其类别<BR>P
= [-3 -2 -2 0 0 0 0 +2 +2 +3;
<BR> 0 +1 -1 +2 +1 -1 -2 +1
-1 0];<BR>C = [1 1 1 2 2 2 2 1 1
1];<BR>%将这些类别转换成学习向量量化网络使用的目标向量<BR>T =
ind2vec(C)<BR>%plotvec函数能够根据其第二个参数的情况用不同的颜色在同一幅图上绘制出第一个参数中对应的向量<BR>plotvec(P,C),<BR>title('输入二维向量');<BR>xlabel('P(1)');<BR>ylabel('P(2)');<BR>%建立网络<BR>net
= newlvq(minmax(P),4,[.6
.4],0.1);<BR>%在同一幅图上绘出输入向量及初始权重向量<BR>figure;<BR>plotvec(P,C)<BR>hold
on<BR>W1=net.iw{1};<BR>plot(W1(1,1),W1(1,2),'ow')<BR>title('输入以及权重向量');<BR>xlabel('P(1),
W(1)');<BR>ylabel('P(2), W(2)');<BR>hold
off;<BR>%训练网络，并再次绘出权重向量<BR>figure;<BR>plotvec(P,C);<BR>hold
on;<BR>net.trainParam.epochs=150;<BR>net.trainParam.show=Inf;<BR>net=train(net,P,T);<BR>plotvec(net.iw{1}',vec2ind(net.lw{2}),'o');<BR>%对于一个特定的点，得到网络的输出<BR>p
= [0.8; 0.3];<BR>a = vec2ind(sim(net,p))<BR><IMG alt=""
src="LVQ模式分类网络的设计.files/20048623411632.gif"
onload="javascript:if(this.width>740)this.width=740"
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src="LVQ模式分类网络的设计.files/200486234111209.gif"
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