Hamiltonian Neural Networks Based Networks for Learning
Wiesław Sieńko, Wiesław Citko · BiblioBoard Library Catalog (Open Research Library) · 2009
The main issue considered in this chapter is the deterministic learning of mappings. The learning method analysed here relies on multivariate function approximations using mainly skew-symmetric kernels, thus giving rise to very large scale classifiers and associative memories. By using HNN-based orthogonal filters, one obtains regularized and stable structures of networks for learning. Hence, classifiers and memories can be implemented for