title: Training trajectories by continuous recurrent multilayer networks.
abstract: This paper addresses the problem of training trajectories by means of continuous recurrent neural networks whose feedforward parts are multilayer perceptrons. Such networks can approximate a general nonlinear dynamic system with arbitrary accuracy. The learning process is transformed into an optimal control framework where the weights are the controls to be determined. A training algorithm based upon a variational formulation of Pontryagin's maximum principle is proposed for such networks. Computer examples demonstrating the efficiency of the given approach are also presented.
authors.affiliation: Inst. of Med. Statistics, Comput. Sci. and Documentation, Friedrich-Schiller-Univ., Jena.
authors.forename: L
authors.initials: L
authors.lastname: Leistritz
authors.forename: M
authors.initials: M
authors.lastname: Galicki
authors.forename: H
authors.initials: H
authors.lastname: Witte
authors.forename: E
authors.initials: E
authors.lastname: Kochs