Unipolar and Bipolar Mathematical Inference of Weight Adjustment Mode of Single Layer Perceptron on AND Logic Gate

In order to ensure that electrical terminals only “switch on” after the proper logic process has been applied, logic gates are employed to make judgments. Each logic gate has a name that explains how various inputs will affect the potential outcomes. To perform logical functions on one or more binary sequence of inputs and produce a single digital output, logic gates are utilized. Embedded systems, microcontrollers, electrical and electronics circuit boards, and programmable logic applications generally make use of logic gates. When the sensor receives no information, it generates low Output impedance for logic 0. It has been determined that using logic gates to create an edge avoider robotics that lowers project budget and speeds up the robot. With the growth of technology, the neural network could also be used for implementing the logic gate using Single Layer Perceptron neural network often termed as Linear Threshold Gate. This paper attempt to analyze the mathematical inference on the number of execution steps for the weight adjustment in the activation function of AND Logic gate. This inference carried out by varying the input of Single layer Perceptron Neural Network in the form of unipolar and bipolar input. The activation function weight adjustment had carried out using batch and sequential mode of execution. Mathematical implementation of AND logic gate exhibits that Bipolar sequential and Bipolar batch mode weight adjustment had less number of execution steps reaching 100% accuracy by matching the desired and predicted output when compared to the unipolar. The detailed execution steps are produced herewith for the validation of outcome.