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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In this paper, we study an offline gradient method with inner-penalty for training multi-output feedforward neural networks. The monotonicity of the error function and weight boundedness for the offline gradient with inner-penalty are presented, both weak and strong convergence results are proved, which will be very meaningful for theoretical research or applications on multi-output neural networks.

Convergence of Offline Gradient Method with Inner-penalty for Multi-output Feedforward Neural Networks