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 work, a method of random parameters generation for randomizedlearning of a single-hidden-layer feedforward neural network is proposed. Themethod firstly, randomly selects the slope angles of the hidden neuronsactivation functions from an interval adjusted to the target function, thenrandomly rotates the activation functions, and finally distributes them acrossthe input space. For complex target functions the proposed method gives betterresults than the approach commonly used in practice, where the randomparameters are selected from the fixed interval. This is because it introducesthe steepest fragments of the activation functions into the input hypercube,avoiding their saturation fragments.

Improving Randomized Learning of Feedforward Neural Networks by Appropriate Generation of Random Parameters