title: A learning algorithm for applying synthesized stable dynamics to system identification.
abstract: In this paper the models discussed by Cohen are extended by introducing an input term. This allows the resulting models to be utilized for system identification tasks. This approach gives a direct way to encode qualitative information such as attractor dimension into the model. We prove that this model is stable in the sense that a bounded input leads to a bounded state when a minor restriction is imposed on the Lyapunov function. By employing this stability result, we are able to find a learning algorithm which guarantees convergence to a set of parameters for which the error between the model trajectories and the desired trajectories vanishes.
authors.affiliation: Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, USA
authors.forename: Gregory L.
authors.initials: GL
authors.lastname: Heileman
authors.forename: Chaouki T.
authors.initials: CT
authors.lastname: Abdallah
authors.forename: James W.
authors.initials: JW
authors.lastname: Howse