Neural Representation of Basic Gates(AND,OR,NOT) (Perceptron Algorithm)

Before we get into the gates topics, we need to understand the concept of Perceptron Algorithm. Here in this image x1,x2,……,xn are being the inputs that will be taken into function where weights and the inputs will be get multiplied and it will be taken into the activation function and the output from the activation function will be classified according to the thresh hold function. How we train the perceptron to get perfect output? One way to learn acceptable weight vector is to begin with random weights then apply iteratively to the each of the training example and modifies the perceptron weights if it misclassifies the output. We will try to understand this through the Mathematical knowledge lets consider the w1 1.2,w2 0.6,learning rate 0.5 and Threshold 1 Whose threshold it is not equal to one so its zero.