顯示函數plotpv的2個參數是輸入向量和目標函數

1)

>> P=[0 2 3 1 5 0 6
????? 2 9 0 7 1 0 0]

P =

???? 0???? 2???? 3???? 1???? 5???? 0???? 6
???? 2???? 9???? 0???? 7???? 1???? 0???? 0

>> t=[0 1 0 1 1 0 0]

t =

???? 0???? 1???? 0???? 1???? 1???? 0???? 0

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>>plotpv(P,T)

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2)

>> P=[0 2 3 1 5 0 6
2 9 0 7 1 0 0
0 9 2 1 8 0 9]

P =

???? 0???? 2???? 3???? 1???? 5???? 0???? 6
???? 2???? 9???? 0???? 7???? 1???? 0???? 0
???? 0???? 9???? 2???? 1???? 8???? 0???? 9

>> T=[1 0 0 0 0 1 0]

T =

???? 1???? 0???? 0???? 0???? 0???? 1???? 0

>> plotpv(P,T)
>>

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3、plotpc返回分界線控點，根據權W和閾值B的輸入繪制一條分界線

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plotpc(W,B)

plotpc(W,B,H)包含從前一次調用中返回的控點，在繪制新分界線之前，刪除舊線

?P=[ -0.3 -0.5 +0.6 -0.1 -0.8
-0.5 +0.6 -0.2 +0.5 -0.6 ]
T=[0 1 1 1 0]
plotpv(P,T)
net=newp([-10 2; -5 20],1)
hold on
linehandle=plotpc(net.iw{1},net.b{1})
net.adaptParam.passes=3
linehandle=plotpc(net.iw{1},net.b{1})
for a=1:25
[net,Y,E]=adapt(net,P,T)
linehandle=plotpc(net.iw{1},net.b{1},linehandle)
drawnow
end

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關于 PLOTPC

?Plot a classification line on a perceptron vector plot.

繪制一個分類線，根據權矩陣W和閾值矩陣B
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?? Syntax
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???? plotpc(W,b)
???? plotpc(W,b,h)
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?? Description
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???? PLOTPC(W,B) takes these inputs,
?????? W - SxR weight matrix (R must be 3 or less).
?????? B - Sx1 bias vector.
???? and returns a handle to a plotted classification line.
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???? PLOTPC(W,B,H) takes these inputs,
?????? H - Handle to last plotted line.
???? and deletes the last line before plotting the new one.
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???? This function does not change the current axis and is intended
???? to be called after PLOTPV.
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?? Example
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???? The code below defines and plots the inputs and targets for a
???? perceptron:
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?????? p = [0 0 1 1; 0 1 0 1];
?????? t = [0 0 0 1];
?????? plotpv(p,t)
?下面的代碼創建一個感知器,感知器是單層神經元，采用閾值激活函數，對一組輸入向量的響應達到 元素為0或1的目標輸出，實現對輸入向量進行分類，下面這個代碼創建了這個分類線
???? The following code creates a perceptron with inputs ranging
???? over the values in P, assigns values to its weights
???? and biases, and plots the resulting classification line.
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?????? net = newp(minmax(p),1);
?????? net.iw{1,1} = [-1.2 -0.5];
?????? net.b{1} = 1;
?????? plotpc(net.iw{1,1},net.b{1})

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我們把這個程序改一下

P=[0 1 0 1 1;1 1 1 0 0]
T=[0 1 0 0 0]
net = newp(minmax(P),1)

plotpv(P,T)

hold on

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生成1個神經元的感知器,其中P是輸入向量,T是目標函數

>> net.b{1}

ans =

???? 0

>> net.iw{1,1}

ans =

???? 0???? 0

>>

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

此時執行沒有意義，因為權值和閾值初始化為0

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我們指定一個正確的權值和閾值

net.b{1}=-2

net.iw{1,1} =[1 1]

?plotpc(net.iw{1,1},net.b{1})

注意plotpc必須要在plotpv函數后執行,可以看到分界線正確顯示出來

凡在分界線左側的都是反例，右側的是正例，即輸出值>0

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matlab-神經網絡-感知器(2)