Neural Networks with R

Perceptron and multilayer architectures

A perceptron is a single neuron that classifies a set of inputs into one of two categories (usually 1 or -1). If the inputs are in the form of a grid, a perceptron can be used to recognize visual images of shapes. The perceptron usually uses a step function, which returns 1 if the weighted sum of the inputs exceeds a threshold, and 0 otherwise.

When layers of perceptron are combined together, they form a multilayer architecture, and this gives the required complexity of the neural network processing. Multi-Layer Perceptrons (MLPs) are the most widely used architecture for neural networks.