A Convergence Result for Learning in Recurrent Neural Networks
Resource
Neural Computation, n.6 p.420-440
Journal
Neural Computation
Journal Issue
6
Pages
420-440
Date Issued
1994-01
Author(s)
Abstract
We give a rigorous analysis of the convergence properties of a backpropagation algorithm for recurrent networks containing either output or hidden layer recurrence. The conditions permit data generated by stochastic processes with considerable dependence. Restrictions are offered that may help assure convergence of the network parameters to a local optimum, as some simulations illustrate.
SDGs
Type
journal article