A type of

neural network that, unlike the

Perceptron networks,

Adaline networks, etc, is flat (single-layer) and

interconnected. This single layer acts as the input and output layer. Of course, this leads to the same number of outputs as inputs. This property, and the fact that an Hopfield net in a

discrete system will always isolate to either a single solution or a cyclic solution (if run

interatively), makes this network fairly useful for image/

pattern recognition.

Unlike Madaline networks, Hopfield nets have a lyapunov exponent of 0, noting that they do not exhibit chaos.