title: Best estimated inverse versus inverse of the best estimator.
abstract: The construction of a feed-forward controller frequently requires the estimation of an inverse function. Two possible methods to achieve this are: (i) learning the best estimated inverse (BEI), a method that is sometimes referred to as direct inverse learning and (ii) learning the inverse of the best estimator (IBE), a method that is sometimes referred to as indirect inverse learning. We analyze a general control problem, in the presence of noise, and show analytically that these two methods are asymptotically significantly different, even for simple linear non-redundant systems. We further demonstrate that the IBE method is typically superior for control purposes.
authors.forename: A
authors.initials: A
authors.lastname: Karniel
authors.forename: R
authors.initials: R
authors.lastname: Meir
authors.forename: G F
authors.initials: GF
authors.lastname: Inbar
mesh_terms: D016477:Artifacts; D002490:Central Nervous System / Q000502:physiology*; D005246:Feedback / Q000502:physiology; D007858:Learning / Q000502:physiology*; D016014:Linear Models; D008959:Models, Neurological; D016571:Neural Networks, Computer