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

Arizona State University

Research output: Contribution to journal › Editorial › peer-review

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
DOIs	https://doi.org/10.1109/tnnls.2022.3161003
Publication status	Published (in print/issue) - 2 May 2022

Bibliographical note

Publisher Copyright:
© 2012 IEEE.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Software

Access to Document

10.1109/tnnls.2022.3161003

Cite this

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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.",

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AU - Si, Jennie

AU - Coleman, Sonya

AU - Kerr, Dermot

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Y1 - 2022/5/2

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KW - Artificial Intelligence

KW - Computer Networks and Communications

KW - Computer Science Applications

KW - Software

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ER -

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

Abstract

Bibliographical note

UN SDGs

Keywords

Access to Document

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