What is a Perceptron? Simple Explanation with Example

What is a Perceptron?

A Perceptron is the simplest type of artificial neural network and is mainly used for binary classification problems. It acts as a basic decision-making unit that takes multiple input values and produces a single output (either 0 or 1).

The perceptron works by multiplying each input with a corresponding weight, adding a bias, and then passing the result through an activation function to generate the final output.

Key Components of a Perceptron

• Inputs: Values provided to the perceptron (x₁, x₂, …).
• Weights: Parameters that determine the importance of each input.
• Bias: A constant value that helps shift the decision boundary.
• Activation Function: Produces the final output based on the weighted sum.

Working of a Perceptron

The perceptron computes a weighted sum of its inputs and adds a bias term:

Output = Activation( w₁x₁ + w₂x₂ + … + bias )

If the computed value is greater than or equal to a threshold, the output is 1; otherwise, the output is 0.

How Does a Perceptron Learn?

A perceptron learns from training data using the Perceptron Learning Rule. After producing an output, it compares the result with the desired (teacher) output.

If the output is incorrect, the perceptron updates its weights to reduce the error. This process is repeated for multiple training examples until the perceptron produces correct outputs.

Limitation of a Perceptron

A major limitation of a perceptron is that it can solve only linearly separable problems. It cannot correctly classify problems like XOR, where data cannot be separated by a single straight line.