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Author Terekhov, Alexander V. ♦ Zatsiorsky, Vladimir M.
Source SpringerLink
Content type Text
Publisher Springer-Verlag
File Format PDF
Copyright Year ©2011
Language English
Subject Domain (in DDC) Technology ♦ Medicine & health
Subject Keyword Inverse optimization ♦ Optimization ♦ Uniqueness Theorem ♦ Cost function ♦ Grasping ♦ Force sharing ♦ Bioinformatics ♦ Neurosciences ♦ Statistical Physics, Dynamical Systems and Complexity ♦ Neurobiology ♦ Computer Application in Life Sciences
Abstract One of the key problems of motor control is the redundancy problem, in particular how the central nervous system (CNS) chooses an action out of infinitely many possible. A promising way to address this question is to assume that the choice is made based on optimization of a certain cost function. A number of cost functions have been proposed in the literature to explain performance in different motor tasks: from force sharing in grasping to path planning in walking. However, the problem of uniqueness of the cost function(s) was not addressed until recently. In this article, we analyze two methods of finding additive cost functions in inverse optimization problems with linear constraints, so-called linear-additive inverse optimization problems. These methods are based on the Uniqueness Theorem for inverse optimization problems that we proved recently (Terekhov et al., J Math Biol 61(3):423–453, 2010). Using synthetic data, we show that both methods allow for determining the cost function. We analyze the influence of noise on the both methods. Finally, we show how a violation of the conditions of the Uniqueness Theorem may lead to incorrect solutions of the inverse optimization problem.
ISSN 03401200
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2011-02-11
Publisher Place Berlin/Heidelberg
e-ISSN 14320770
Journal Biological Cybernetics
Volume Number 104
Issue Number 1
Page Count 19
Starting Page 75
Ending Page 93


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Source: SpringerLink