Relating the slope of the activation function and the learning rate within a recurrent neural network

Danilo P. Mandic, Jonathon A. Chambers

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)


A relationship between the learning rate ? in the learning algorithm, and the slope ß in the nonlinear activation function, for a class of recurrent neural networks (RNNs) trained by the real-time recurrent learning algorithm is provided. It is shown that an arbitrary RNN can be obtained via the referent RNN, with some deterministic rules imposed on its weights and the learning rate. Such relationships reduce the number of degrees of freedom when solving the nonlinear optimization task of finding the optimal RNN parameters.
Original languageEnglish
Pages (from-to)1069-1077
Number of pages9
JournalNeural Computation
Issue number5
Publication statusPublished - 1999

Cite this