自組織競爭網路

net= newc(P,S,KLR,CLR) 

P  - RxQ matrix of Q input vectors.

S  - Number of neurons.

KLR - Kohonen learning rate, default = 0.01. 

CLR - Conscience learning rate, default = 0.001.

在2010b以後的版本用competlayer代替。

descriptions:Competitive layers learn to classify input vectors into a given number of classes, according to similarity
between vectors, with a preference for equal numbers of vectors per class.

net=competlayer(numClasses,kohonenLR,conscienceLR) 

numClasses:Number of classes to classify inputs (default = 5)

kohonenLR:Learning rate for Kohonen weights (default = 0.01)

conscienceLR：Learning rate for conscience bias (default = 0.001)

Examples：
inputs = iris_dataset;
net = competlayer(6); 

%net=init(net);
net = train(net,inputs);
view(net)
outputs = net(inputs);
classes = vec2ind(outputs);

PS:在建立網路的過程中，需要注意初始化網路，即在訓練之前可以加net=init(net);