Implementation of Perceptron Algorithm for Logical OR and Logical AND Functions

The perceptron algorithm effectively implements logical operations, including logical OR and logical AND functions. As a learning algorithm based on artificial neural networks, the perceptron performs classification and decision-making by applying weighted sums and threshold processing to input signals. Through the perceptron algorithm, we successfully achieve logical OR and logical AND functionalities, thereby enhancing system flexibility and performance.

Implementation approach: The perceptron model utilizes weight adjustment during training to separate linearly separable functions. For logical OR (returns true if any input is true) and logical AND (returns true only if all inputs are true), the algorithm initializes random weights and bias, then iteratively updates them using the perceptron learning rule: w = w + η*(target - output)*input, where η is the learning rate. Key functions include forward propagation for calculation and backward propagation for weight updates.

Code considerations: The implementation typically involves defining activation functions (like step function), setting appropriate thresholds, and handling binary inputs (0,1). The algorithm converges efficiently for these fundamental logic operations since they are linearly separable, making them ideal cases for demonstrating perceptron capabilities.