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.
|Neuron layers||1 matrix|
|Input value types||binary|
|Activation function||signum / hard limiter|
|Learning algorithm||delta learning rule, simulated annealing (mostly used)|
|Mainly used in||pattern association, optimization problems|