Simple recurrent networks as generalized hidden Markov models with distributed representations
Yasubumi Sakakibara, Mostefa Golea · 2002
Proposes simple recurrent neural networks as probabilistic models for representing and predicting time-sequences. The proposed model has the advantage of providing forecasts that consist of probability densities instead of single guesses of future values. It turns out that the model can be viewed as a generalized hidden Markov model with a distributed representation. The authors devise an efficient learning algorithm for estimating the parameters of the model using dynamic programming. The authors present some very preliminary simulation results to demonstrate the potential capabilities of the model. The present analysis provides a new probabilistic formulation of learning in simple recurrent networks.