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Published in:
2007 | OriginalPaper | Chapter
Neural Learning Algorithms Based on Mappings: The Case of the Unitary Group of Matrices
Author : Simone Fiori
Published in: Artificial Neural Networks – ICANN 2007
Publisher: Springer Berlin Heidelberg
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Neural learning algorithms based on optimization on manifolds differ by the way the single learning steps are effected on the neural system’s parameter space. In this paper, we present a class counting four neural learning algorithms based on the differential geometric concept of mappings from the tangent space of a manifold to the manifold itself. A learning stepsize adaptation theory is proposed as well under the hypothesis of additiveness of the learning criterion. The numerical performances of the discussed algorithms are illustrated on a learning task and are compared to a reference algorithm known from literature.