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

Random functional-linked types of neural networks (RFLNNs), e.g., the extremelearning machine (ELM) and broad learning system (BLS), which avoid sufferingfrom a time-consuming training process, offer an alternative way of learning indeep structure. The RFLNNs have achieved excellent performance in variousclassification and regression tasks, however, the properties and explanationsof these networks are ignored in previous research. This paper gives someinsights into the properties of RFLNNs from the viewpoints of frequency domain,and discovers the presence of frequency principle in these networks, that is,they preferentially capture low-frequencies quickly and then fit the highfrequency components during the training process. These findings are valuablefor understanding the RFLNNs and expanding their applications. Guided by thefrequency principle, we propose a method to generate a BLS network with betterperformance, and design an efficient algorithm for solving Poison's equation inview of the different frequency principle presenting in the Jacobi iterativemethod and BLS network.

Properties and Potential Applications of Random Functional-Linked Types of Neural Networks