W1,W2,W3,W4 are weight values normalized in the range of either (0,1) or (-1,1) and associated with each input line, SUM is the weighted sum, and T is a threshold constant. The function
f is a linear step function at threshold T as shown in figure 2.3. The symbolic representation of the linear threshold gate is shown in figure 2.4
The McCulloch-Pitts model of a neuron is simple yet has substantialcomputing potential. It also has a precise mathematical definition. However,this model is so simplistic that it only generates a binary output and also theweight and threshold values are fixed. The neural computing algorithm has diverse features for various applications [Zur92]. Thus, we need to obtain the neural model with more flexible computational features.