Add to ORKGHammer B. On the approximation capability of recurrent neural networks. Neurocomputing. 2000;31(1-4):107-123
AbstractIn this work, some ubiquitous neural networks are applied to model the landscape of a known ...
Abstract—The problem of approximating functions by neural networks using incremental algorithms is s...
This monograph is the continuation and completion of the monograph, “Intelligent Systems: Approximat...
Hammer B. On the Approximation Capability of Recurrent Neural Networks. In: Heiss M, ed. Proceedings...
Hammer B. On the generalization ability of simple recurrent neural networks. Osnabrücker Schriften z...
Hammer B. On the Generalization Ability of Recurrent Networks. In: Dorffner G, Bischof H, Hornik K, ...
In this paper, the proofs of approximation on neural network given by T.Chen and Ch.Jiang was revise...
Hammer B. Approximation capabilities of folding networks. In: Verleysen M, ed. European Symposium on...
We examine the approximating power of recurrent networks for dynamical systems through an unbounded ...
de Raedt L, Hammer B, Hitzler P, Maass W, eds. Recurrent Neural Networks - Models, Capacities, and A...
Approximation of highly nonlinear functions is an important area of computational intelligence. The ...
Hammer B, Sperschneider V. Neural networks can approximate mappings on structured objects. In: Wang ...
Thesis (Ph. D.)--University of Hawaii at Manoa, 1992.Includes bibliographical references (leaves 144...
In this paper, a linear approximation for Gelenbe's Learning Algorithm developed for training Recurr...
Copyright © 2013 F. Zeng and Y. Tang.This is an open access article distributed under the Creative C...
AbstractIn this work, some ubiquitous neural networks are applied to model the landscape of a known ...
Abstract—The problem of approximating functions by neural networks using incremental algorithms is s...
This monograph is the continuation and completion of the monograph, “Intelligent Systems: Approximat...
Hammer B. On the Approximation Capability of Recurrent Neural Networks. In: Heiss M, ed. Proceedings...
Hammer B. On the generalization ability of simple recurrent neural networks. Osnabrücker Schriften z...
Hammer B. On the Generalization Ability of Recurrent Networks. In: Dorffner G, Bischof H, Hornik K, ...
In this paper, the proofs of approximation on neural network given by T.Chen and Ch.Jiang was revise...
Hammer B. Approximation capabilities of folding networks. In: Verleysen M, ed. European Symposium on...
We examine the approximating power of recurrent networks for dynamical systems through an unbounded ...
de Raedt L, Hammer B, Hitzler P, Maass W, eds. Recurrent Neural Networks - Models, Capacities, and A...
Approximation of highly nonlinear functions is an important area of computational intelligence. The ...
Hammer B, Sperschneider V. Neural networks can approximate mappings on structured objects. In: Wang ...
Thesis (Ph. D.)--University of Hawaii at Manoa, 1992.Includes bibliographical references (leaves 144...
In this paper, a linear approximation for Gelenbe's Learning Algorithm developed for training Recurr...
Copyright © 2013 F. Zeng and Y. Tang.This is an open access article distributed under the Creative C...
AbstractIn this work, some ubiquitous neural networks are applied to model the landscape of a known ...
Abstract—The problem of approximating functions by neural networks using incremental algorithms is s...
This monograph is the continuation and completion of the monograph, “Intelligent Systems: Approximat...