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Perceptron Learning Rule

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t = -1. t = 1. o =1. w = [0.25 –0.1 0.5] x 2 = 0.2 x 1 – 0.5. o =-1. ( x , t )=([2,1], - 1) o =sgn(0.45-0.6+0.3) =1. ( x , t )=([-1,-1], 1) o = sgn(0.25+0.1-0.5) = - 1.  w = [0.2 –0.2 –0.2].  w = [ - 0.2 –0.4 –0.2]. ( x , t )=([1,1], 1) o = sgn(0.25 - 0.7+0.1) = - 1.

Perceptron Learning Rule

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t=-1 t=1 o=1 w=[0.25 –0.1 0.5] x2 = 0.2 x1 – 0.5 o=-1 (x,t)=([2,1],-1) o=sgn(0.45-0.6+0.3) =1 (x,t)=([-1,-1],1) o=sgn(0.25+0.1-0.5) =-1 w=[0.2 –0.2 –0.2] w=[-0.2 –0.4 –0.2] (x,t)=([1,1],1) o=sgn(0.25-0.7+0.1) =-1 w=[0.2 0.2 0.2] -0.5x1+0.3x2+0.45>0  o = 1 Perceptron Learning Rule x2 x2 x1 x1 x2 x2 x1 x1

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