title: Simple and conditioned adaptive behavior from Kalman filter trained recurrent networks.
abstract: We illustrate the ability of a fixed-weight neural network, trained with Kalman filter methods, to perform tasks that are usually entrusted to an explicitly adaptive system. Following a simple example, we demonstrate that such a network can be trained to exhibit input-output behavior that depends on which of two conditioning tasks was performed a substantial number of time steps in the past. This behavior can also be made to survive an intervening interference task.
authors.affiliation: Research and Advanced Engineering, Ford Motor Company, Dearborn, MI, USA.
authors.forename: Lee A
authors.initials: LA
authors.lastname: Feldkamp
authors.forename: Danil V
authors.initials: DV
authors.lastname: Prokhorov
authors.forename: Timothy M
authors.initials: TM
authors.lastname: Feldkamp
mesh_terms: D000223:Adaptation, Psychological / Q000502:physiology; D003213:Conditioning, Psychological / Q000502:physiology; D016571:Neural Networks, Computer