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ICTAI
2008
IEEE
13 years 11 months ago
The Performance of Approximating Ordinary Differential Equations by Neural Nets
—The dynamics of many systems are described by ordinary differential equations (ODE). Solving ODEs with standard methods (i.e. numerical integration) needs a high amount of compu...
Josef Fojdl, Rüdiger W. Brause
JCNS
1998
134views more  JCNS 1998»
13 years 4 months ago
Analytical and Simulation Results for Stochastic Fitzhugh-Nagumo Neurons and Neural Networks
An analytical approach is presented for determining the response of a neuron or of the activity in a network of connected neurons, represented by systems of nonlinear ordinary stoc...
Henry C. Tuckwell, Roger Rodriguez
NIPS
1990
13 years 6 months ago
Convergence of a Neural Network Classifier
In this paper, we show that the LVQ learning algorithm converges to locally asymptotic stable equilibria of an ordinary differential equation. We show that the learning algorithm ...
John S. Baras, Anthony LaVigna
FORMATS
2006
Springer
13 years 8 months ago
On the Computational Power of Timed Differentiable Petri Nets
Abstract. Well-known hierarchies discriminate between the computational power of discrete time and space dynamical systems. A contrario the situation is more confused for dynamical...
Serge Haddad, Laura Recalde, Manuel Silva
QEST
2005
IEEE
13 years 10 months ago
Fluid Flow Approximation of PEPA models
In this paper we present a novel performance analysis technique for large-scale systems modelled in the stochastic process algebra PEPA. In contrast to the well-known approach of ...
Jane Hillston