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Brian D. O. Anderson
Researcher at Australian National University
Publications - 1120
Citations - 50069
Brian D. O. Anderson is an academic researcher from Australian National University. The author has contributed to research in topics: Linear system & Control theory. The author has an hindex of 96, co-authored 1107 publications receiving 47104 citations. Previous affiliations of Brian D. O. Anderson include University of Newcastle & Eindhoven University of Technology.
Papers
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Proceedings ArticleDOI
Hilbert transforms from interpolation data
TL;DR: In this article, the authors studied the generation of stable transfer functions for which the real or imaginary part takes prescribed values at discrete uniformly spaced points on the unit circle, and presented formulas bounding the error between a particular interpolating function and any function consistent with the data.
Proceedings ArticleDOI
Application of real rational modules in system identification
TL;DR: A real rational module framework is introduced in the context of prediction error identification using Box-Jenkins model structures to solve and/or extend a number of problems related to the computation of error norms that arise in system identification.
Journal ArticleDOI
New Results on Stationary Stochastic Feedback Processes
TL;DR: In this article, the authors consider stationary stochastic discrete-time vector processes made up of two components processes y and u, such that the joint (y, u)-process has a rational spectral density ϕ yu (z).
Proceedings ArticleDOI
Unique Maximum Likelihood Localization of Nuclear Sources
Brian D. O. Anderson,Soura Dasgupta,Henry E. Baidoo-Williams,M. F. Anjum,Raghuraman Mudumbai +4 more
TL;DR: Using Morse theory it is shown that not only is the likelihood function unimodal on either side of the line the sensor moves on, but that in fact it has only one critical point in each side and this critical point is the global maximum.
Proceedings Article
Safe nonlinear controller switching with biomedical engineering applications
TL;DR: In this article, the use of a known nonlinear controller appears attractive to replace the existing controller, on the basis of limited knowledge of the plant, and the authors apply their novel databased verification tests for ensuring that the introduction of the new non-linear controller will stabilize the true plant for two different practical scenarios.