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TNN
1998
146views more  TNN 1998»
13 years 5 months ago
An analytical framework for local feedforward networks
Interference in neural networks occurs when learning in one area of the input space causes unlearning in another area. Networks that are less susceptible to interference are refer...
S. Weaver, L. Baird, Marios M. Polycarpou
TNN
1998
89views more  TNN 1998»
13 years 5 months ago
Fast training of recurrent networks based on the EM algorithm
— In this work, a probabilistic model is established for recurrent networks. The EM (expectation-maximization) algorithm is then applied to derive a new fast training algorithm f...
Sheng Ma, Chuanyi Ji
TNN
1998
146views more  TNN 1998»
13 years 5 months ago
Fuzzy lattice neural network (FLNN): a hybrid model for learning
— This paper proposes two hierarchical schemes for learning, one for clustering and the other for classification problems. Both schemes can be implemented on a fuzzy lattice neu...
Vassilios Petridis, Vassilis G. Kaburlasos
TNN
1998
77views more  TNN 1998»
13 years 5 months ago
Self-organization of spiking neurons using action potential timing
Berthold Ruf, Michael Schmitt
TNN
1998
111views more  TNN 1998»
13 years 5 months ago
Asymptotic distributions associated to Oja's learning equation for neural networks
— In this paper, we perform a complete asymptotic performance analysis of the stochastic approximation algorithm (denoted subspace network learning algorithm) derived from Oja’...
Jean Pierre Delmas, Jean-Francois Cardos
TNN
1998
254views more  TNN 1998»
13 years 5 months ago
Comparative analysis of fuzzy ART and ART-2A network clustering performance
—Adaptive resonance theory (ART) describes a family of self-organizing neural networks, capable of clustering arbitrary sequences of input patterns into stable recognition codes....
T. Frank, Karl-Friedrich Kraiss, Torsten Kuhlen
TNN
1998
96views more  TNN 1998»
13 years 5 months ago
Artificial neural networks for solving ordinary and partial differential equations
Isaac E. Lagaris, Aristidis Likas, Dimitrios I. Fo...
TNN
1998
100views more  TNN 1998»
13 years 5 months ago
A dynamical system perspective of structural learning with forgetting
—Structural learning with forgetting is an established method of using Laplace regularization to generate skeletal artificial neural networks. In this paper we develop a continu...
D. A. Miller, J. M. Zurada