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 aim of this paper is to train an RBF neural network and select centersunder concurrent faults. It is well known that fault tolerance is a veryattractive property for neural networks. And center selection is an importantprocedure during the training process of an RBF neural network. In this paper,we devise two novel algorithms to address these two issues simultaneously. Bothof them are based on the ADMM framework. In the first method, the minimaxconcave penalty (MCP) function is introduced to select centers. In the secondmethod, an l0-norm term is directly used, and the hard threshold (HT) isutilized to address the l0-norm term. Under several mild conditions, we canprove that both methods can globally converge to a unique limit point.Simulation results show that, under concurrent fault, the proposed algorithmsare superior to many existing methods.

l0-norm Based Centers Selection for Training Fault Tolerant RBF Networks and Selecting Centers