Improving generalization capabilities of dynamic neural networks.
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.
Galicki
Miroslaw
M
Institute of Medical Statistics, Computer Sciences and Documentation, Friedrich Schiller University, Jena, Germany. galicki@imsid.uni-jena.de
Leistritz
Lutz
L
Zwick
Ernst Bernhard
EB
Witte
Herbert
H
D000465:Algorithms; D003198:Computer Simulation; D008959:Models, Neurological; D016571:Neural Networks, Computer