Editorial Biologically Learned/Inspired Methods for Sensing, Control, and Decision

Yongduan SONG; Jennie SI; Sonya COLEMAN; Dermot KERR

doi:10.1109/TNNLS.2022.3161003

Editorial Biologically Learned/Inspired Methods for Sensing, Control, and Decision

Yongduan SONG^*
, Jennie SI
, Sonya COLEMAN
, Dermot KERR

^*Corresponding author for this work

Research output: Journal Publications › Editorial/Preface (Journal)

8 Citations (Scopus)

Abstract

The Special Issue aims at collecting new ideas and contributions at the frontier of bridging the gap between biological and engineering systems. Contributions include a wide range of related research topics, from neural computing to adaptive control and cooperative control, from autonomous decision systems to mathematical and computational models, and from neuropsychology-based decision and control to engineering system sensing and control algorithms, as well as applications and case studies of biologically inspired systems. This editorial note provides a brief overview of the accepted articles.

Original language	English
Pages (from-to)	1820-1824
Number of pages	5
Journal	IEEE Transactions on Neural Networks and Learning Systems
Volume	33
Issue number	5
Early online date	2 May 2022
DOIs	https://doi.org/10.1109/TNNLS.2022.3161003
Publication status	Published - May 2022
Externally published	Yes

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Publisher Copyright:
© 2012 IEEE.

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Editorial Biologically Learned/Inspired Methods for Sensing, Control, and Decision

Abstract

Bibliographical note

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