ADALINE (Adaptive Linear Neuron or later Adaptive Linear Element) is an early single-layer artificial neural network and the name of the physical device that implemented this network. The problem here is to implement AND-NOT using Adaline network. Here we perform 5 epochs of training and calculate total mean error in each case, the total mean error decreases after each epochs and later becomes nearly constant.
The Truth Table for AND-NOT Gate is as follows: x1 x2 t 1 1 -1 1 -1 1 -1 1 -1 -1 -1 -1
EPOCH 1 Errors 2.56 1.2544 0.430336 1.47088 Total Mean Error :5.71562 EPOCH 2 Errors 0.951327 0.569168 0.106353 0.803357 Total Mean Error :2.43021 EPOCH 3 Errors 0.617033 0.494715 0.369035 0.604961 Total Mean Error :2.08574 EPOCH 4 Errors 0.535726 0.496723 0.470452 0.541166 Total Mean Error :2.04407 EPOCH 5 Errors 0.515577 0.503857 0.499932 0.520188 Total Mean Error :2.03955
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