Partially recurrent neural networks for production of temporal sequence
A.F.R. Araujo, H. D'Arbo · 2002
This paper proposes six partially recurrent neural network architectures to evaluate the roles played by interlayer and intralayer feedback connections in producing a temporal sequence of states. The models are divided in two groups according to number of interlayer feedback connections: the first three architectures have nontrainable one-to-one connections, while the last three models have adaptable all-to-all links. Each group has two options for intralayer connections location: either in the input or in hidden layer. The results suggest good performance for planning in different levels of complexity. However, the results suggest the models have poor generalization power.