TY - JOUR
T1 - Editorial Biologically Learned/Inspired Methods for Sensing, Control, and Decision
AU - Song, Yongduan
AU - Si, Jennie
AU - Coleman, Sonya
AU - Kerr, Dermot
N1 - Publisher Copyright:
© 2012 IEEE.
PY - 2022/5/1
Y1 - 2022/5/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85130638256&partnerID=8YFLogxK
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U2 - 10.1109/TNNLS.2022.3161003
DO - 10.1109/TNNLS.2022.3161003
M3 - Review article
AN - SCOPUS:85130638256
SN - 2162-237X
VL - 33
SP - 1820
EP - 1824
JO - IEEE Transactions on Neural Networks and Learning Systems
JF - IEEE Transactions on Neural Networks and Learning Systems
IS - 5
ER -