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.
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 |