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

Engineering, Ira A. Fulton Schools of (IAFSE)

Research output: Contribution to journal › Review article › peer-review

2 Scopus citations

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 (US)
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
State	Published - May 1 2022

ASJC Scopus subject areas

Software
Computer Science Applications
Computer Networks and Communications
Artificial Intelligence

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

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