English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The capability of recurrent neural networks to approximate trajectories of arandom dynamical system, with random inputs, on non-compact domains, and overan indefinite or infinite time horizon is considered. The main result statesthat certain random trajectories over an infinite time horizon may beapproximated to any desired accuracy, uniformly in time, by a certain class ofdeep recurrent neural networks, with simple feedback structures. Theformulation here contrasts with related literature on this topic, much of whichis restricted to compact state spaces and finite time intervals. The modelconditions required here are natural, mild, and easy to test, and the proof isvery simple.

Universal Time-Uniform Trajectory Approximation for Random Dynamical Systems with Recurrent Neural Networks