线性网络分析
% 线性网络分析
clc;
clear;
p=[1.0 1.5 3.0 -1.2];
t=[0.5 1.1 3.0 -1.0];
net = newlind(p,t); %设计网络
% Design a linear layer
w=net.iw{1,1}; %求网路训练结束后的网络权值和偏差
b=net.b{1};
%使用网络
y=sim(net,p) %输出结果
sse=SSE(t-y) %求误差的平方和
% Sum squared error performance function
% sse is a network performance function.
% It measures performance according to the sum of squared errors.
% sse(E,X,PP) takes from one to three arguments,
% E -- Matrix or cell array of error vector(s)
% X -- Vector of all weight and bias values (ignored)
% PP -- Performance parameters (ignored)
% and returns the sum squared error.
% sse(E,net,PP) can take an alternate argument to X,
% net -- Neural network from which X can be obtained (ignored)
% sse(code) returns useful information for each code string:
% 'deriv' -- Name of derivative function
% 'name' -- Full name
% 'pnames' -- Names of training parameters
% 'pdefaults' -- Default training parameters
%作网络训练结果图
a=[t;y];
c=[1,1,1,1];
plotpv(a,c);
% Plot perceptron input/target vectors
% plotpv(P,T) take these inputs,
% P -- R x Q matrix of input vectors (R must be 3 or less)
% T -- S x Q matrix of binary target vectors (S must be 3 or less)
% and plots column vectors in P with markers based on T
% plotpv(P,T,V) takes an additional input,
% V -- Graph limits = [x_min x_max y_min y_max]
% and plots the column vectors with limits set by V
% Examples
%
% The code below defines and plots the inputs and targets for a perceptron:
% p = [0 0 1 1; 0 1 0 1];
% t = [0 0 0 1];
% plotpv(p,t)
%
% 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. 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})
w=[-w,[1]]; %将W变为两维
plotpc(w,-b);
% Plot a classification line on a perceptron vector plot
