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

We consider two high-order tuners that have been shown to have acceleratedperformance, one based on Polyak's heavy ball method and another based onNesterov's acceleration method. We show that parameter estimates are boundedand converge to the true values exponentially fast when the regressors arepersistently exciting. Simulation results corroborate the acceleratedperformance and accelerated learning properties of these high-order tuners incomparison to algorithms based on normalized gradient descent.

Accelerated Performance and Accelerated Learning with Discrete-Time High-Order Tuners