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

This paper proposes an immunity-based RBF training algorithm for nonlinear dynamic problems. Exploiting the locally-tuned structure of RBF net- work through immunological metaphor, a two-stage learning technique is proposed to conflgure RBF centers and widths in the hidden layer. Inspired by a-nity mat- uration process of immune response, immune evolutionary mechanism (IEM) with memory operations is implemented in the learning stages to dynamically flne-tune the network performance. Experiment results also demonstrate that the algorithm has reached good performance with relatively low computational efiorts in dynamic environments.

A TWO-STAGE LEARNING METHOD TO CONFIGURE RBF CENTERS AND WIDTHS IN DYNAMIC ENVIRONMENT EMPLOYING IMMUNE OPERATIONS