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%控制感知機的學習過程,學習AND運算
P=[0 1 0 1 1;1 1 1 0 0];
T=[0 1 0 0 0];
net = newp([0 1;0 1],1);
net=init(net);

y=sim(net,P);
e=T-y;
while (mae(e)>0.0015)
?? dw=learnp(w,P,[],[],[],[],e,[],[],[],[],[])
?? db=learnp(b,ones(1,5),[],[],[],[],e,[],[],[],[],[])
?? %每次學習完后，會返回需要的調整權值矩陣和閾值矩陣
?? w=w+dw
?? b=b+db
?? net.iw{1,1}=w
?? net.b{1}=b??
?? y=sim(net,P);
?? e=T-y
end

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learnp用于感知器神經網絡權值和閾值的學習，學習規則是調整網絡的權值和閾值，使網絡平均絕對誤差性能最小，以便實現輸入向量的分類

help learnp
?LEARNP Perceptron weight/bias learning function.
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?? Syntax
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???? [dW,LS] = learnp(W,P,Z,N,A,T,E,gW,gA,D,LP,LS)
???? [db,LS] = learnp(b,ones(1,Q),Z,N,A,T,E,gW,gA,D,LP,LS)
???? info = learnp(code)
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?? Description
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???? LEARNP is the perceptron weight/bias learning function.
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???? LEARNP(W,P,Z,N,A,T,E,gW,gA,D,LP,LS) takes several inputs,
?????? W? - SxR weight matrix (or b, an Sx1 bias vector).
?????? P? - RxQ input vectors (or ones(1,Q)).
?????? Z? - SxQ weighted input vectors.
?????? N? - SxQ net input vectors.
?????? A? - SxQ output vectors.
?????? T? - SxQ layer target vectors.
?????? E? - SxQ layer error vectors.
?????? gW - SxR gradient with respect to performance.
?????? gA - SxQ output gradient with respect to performance.
?????? D? - SxS neuron distances.
?????? LP - Learning parameters, none, LP = [].
?????? LS - Learning state, initially should be = [].
???? and returns,
?????? dW - SxR weight (or bias) change matrix.
?????? LS - New learning state.
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???? LEARNP(CODE) returns useful information for each CODE string:
?????? 'pnames'??? - Returns names of learning parameters.
?????? 'pdefaults' - Returns default learning parameters.
?????? 'needg'???? - Returns 1 if this function uses gW or gA.
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?? Examples
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???? Here we define a random input P and error E to a layer
???? with a 2-element input and 3 neurons.
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?????? p = rand(2,1);
?????? e = rand(3,1);
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???? Since LEARNP only needs these values to calculate a weight
???? change (see Algorithm below), we will use them to do so.
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?????? dW = learnp([],p,[],[],[],[],e,[],[],[],[],[])
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?? Network Use
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???? You can create a standard network that uses LEARNP with NEWP.
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???? To prepare the weights and the bias of layer i of a custom network
???? to learn with LEARNP:
???? 1) Set NET.trainFcn to 'trainb'.
??????? (NET.trainParam will automatically become TRAINB's default parameters.)
???? 2) Set NET.adaptFcn to 'trains'.
??????? (NET.adaptParam will automatically become TRAINS's default parameters.)
???? 3) Set each NET.inputWeights{i,j}.learnFcn to 'learnp'.
??????? Set each NET.layerWeights{i,j}.learnFcn to 'learnp'.
??????? Set NET.biases{i}.learnFcn to 'learnp'.
??????? (Each weight and bias learning parameter property will automatically
??????? become the empty matrix since LEARNP has no learning parameters.)
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???? To train the network (or enable it to adapt):
???? 1) Set NET.trainParam (NET.adaptParam) properties to desired values.
???? 2) Call TRAIN (ADAPT).
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???? See NEWP for adaption and training examples.
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?? Algorithm
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???? LEARNP calculates the weight change dW for a given neuron from the
???? neuron's input P and error E according to the perceptron learning rule:
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?????? dw =? 0,? if e =? 0
????????? =? p', if e =? 1
????????? = -p', if e = -1
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???? This can be summarized as:
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?????? dw = e*p'

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>> plotpv(P,T)
>> plotpc(net.iw{1,1},net.b{1})

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matlab-神經網絡-感知機(6)