Java Jordan Neural Network

The Jordan Neural Network is a simple recurrent network (SRN) developed by Michael I. Jordan in 1986. The context layer holds the previous output from the output layer and then echos that value back to the hidden layer's input. The hidden layer then always receives input from the previous iteration's output layer. Jordan neural networks are generally trained using genetic, simulated annealing, or one of the propagation techniques. Jordan neural networks are typically used for prediction.

Jordan, M.I. (1986). Serial order: A parallel distributed processing approach (Tech. Rep. No. 8604). San Diego: University of California, Institute for Cognitive Science.

The Jordan Neural Network modeled in the Encog Workbench looks like this: