title: Improving generalization capabilities of dynamic neural networks.
abstract: This work addresses the problem of improving the generalization capabilities of continuous recurrent neural networks. The learning task is transformed into an optimal control framework in which the weights and the initial network state are treated as unknown controls. A new learning algorithm based on a variational formulation of Pontrayagin's maximum principle is proposed. Under reasonable assumptions, its convergence is discussed. Numerical examples are given that demonstrate an essential improvement of generalization capabilities after the learning process of a dynamic network.
authors.affiliation: Institute of Medical Statistics, Computer Sciences and Documentation, Friedrich Schiller University, Jena, Germany. galicki@imsid.uni-jena.de
authors.forename: Miroslaw
authors.initials: M
authors.lastname: Galicki
authors.forename: Lutz
authors.initials: L
authors.lastname: Leistritz
authors.forename: Ernst Bernhard
authors.initials: EB
authors.lastname: Zwick
authors.forename: Herbert
authors.initials: H
authors.lastname: Witte
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D008959:Models, Neurological; D016571:Neural Networks, Computer