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%控制感知機(jī)的學(xué)習(xí)過(guò)程,學(xué)習(xí)AND運(yùn)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(xué)習(xí)完后，會(huì)返回需要的調(diào)整權(quán)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(jīng)網(wǎng)絡(luò)權(quán)值和閾值的學(xué)習(xí)，學(xué)習(xí)規(guī)則是調(diào)整網(wǎng)絡(luò)的權(quán)值和閾值，使網(wǎng)絡(luò)平均絕對(duì)誤差性能最小，以便實(shí)現(xiàn)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(jīng)網(wǎng)絡(luò)-感知機(jī)(6)