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Today, I would like to share with you the relevant knowledge of how the MATLAB linear neural network realizes nonlinear classification. The content is detailed and the logic is clear. I believe most people still know too much about this knowledge, so share this article for your reference. I hope you can get something after reading this article. Let's take a look at it.

The core idea of Madaline is to use multiple linear neurons to get the output respectively, and then judge the output value to get the final classification result.

%% first neuron

P _ 1 = [0re0 _ 0 _ (1) _ (1) _

Net1=linearlayer (0memlr);% create linear network net1=train (net1,P1,d1);% linear network training

%% second neuron P2= [0Magne0Person1Mage1];% input vector d2 = [1ZOBE1];% expected output vector lr=maxlinlr (P2paramount bias`);% calculate the maximum learning rate according to input matrix

Net2=linearlayer (0Magnetil);% create linear network net2=train (net2,P2,d2);% linear network training Y1=sim (net1,P1); Y1=Y1 > = 0.5 × Y2yogsim (net2,P2); Y2=Y2 > = 0.5 possim ~ (Y1&Y2);% display plot ([0LJ 1], [0jue 1], 'bo');% graphic window output hold on;plot ([0LJ 1], [1J 0],' d'); Xanthine 2phon. Y1=1/2/w1 (2)-W1 (1) / W1 (2) * x-w1 (3) / W1 (2);% the first line, 1 prime 2 is the threshold plot for distinguishing 0 from 1 (xmemy y1 Lindsay'); y2=1/2/w2 (2)-W2 (1) / W2 (2) * x-w2 (3) / w2 (2);% second straight line, 1tag 2 is the threshold plot for distinguishing 0 from 1. Axis xlabel ('x'); ylabel ('y'); title ('Madaline is used to solve XOR logic') legend ('0pere1', 'second line'). These are all the contents of this article entitled "how to realize nonlinear classification by MATLAB linear neural network". Thank you for reading! I believe you will gain a lot after reading this article. The editor will update different knowledge for you every day. If you want to learn more knowledge, please pay attention to the industry information channel.

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