Hopfield Net


The Hopfield Net was first introduced by physicist J.J. Hopfield in 1982 and belongs to neural net types which are called "thermodynamical models".

It consists of a set of neurons, where each neuron is connected to each other neuron. There is no differentiation between input and output neurons.

The main application of a Hopfield Net is the storage and recognition of patterns, e.g. image files.

Sample Structure

Hopfield Net
Hopfield Net

Type feedback
Neuron layers 1 matrix
Input value types binary
Activation function signum / hard limiter
Learning method unsupervised
Learning algorithm delta learning rule, simulated annealing (mostly used)
Mainly used in pattern association, optimization problems
Neural Net Components in an Object Oriented Class Structure