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

A radial basis process neural networks can be established ,which is based on expanding the traditional radial basis function neural network to the time domain. Combined with the excellent characteristics of cloud model transformation between qualitative and quantitative , a improved shuffled flog leaping algorithm based on cloud model theory is presented. It is applied to training the radial basis process neural network .The neural network after optimization is used in pumping unit fault diagnosis. The diagnostic results between the CCHSFLA and BP algorithm were compared. The conclusion is that the RBPNN based on CCHSFLA has better training performance, faster convergence rate and higher accuracy.

Training and Application of Radial-Basis Process Neural Network Based on Improved Shuffled Flog Leaping Algorithm