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Approximation Property
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Generalized shifts: unpredictability and undecidability in dynamical systems Nonlinearity 4 199230
A recurrent neural network for modelling dynamical systems
On the approximate realization of continuous mappings by neural networks
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Dynamical approximation by recurrent neural networks
Dynamical approximation by recurrent neural networks,10.1016/S09252312(99)001149,Neurocomputing,Max H. Garzon,Fernanda Botelho
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Dynamical approximation by recurrent neural networks
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Citations: 10
)
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Max H. Garzon
,
Fernanda Botelho
We examine the approximating power of recurrent networks for dynamical systems through an unbounded number of iterations. It is shown that the natural family of recurrent neural networks with saturated linear transfer functions and synaptic weight matrices of rank 1 are essentially equivalent to feedforward neural networks with recurrent layers. Therefore, they inherit the universal
approximation property
of realvalued functions in one variable in a stronger sense, namely through an unbounded number of iterations and approximation guaranteed to be within O(1/n), with n neurons and possibly lateral synapses allowed in the hiddenlayer. However, they are not as complex in their dynamical behavior as systems defined by Turing machines. It is further proved that every continuous dynamical system can be approximated through all iterations, by both finite analog and boolean networks, when one requires approximation of given arbitrary exact orbits of the (perhaps unknown) map. This result no longer holds when the orbits of the given map are only available as contaminated orbits of the approximant net due to the presence of
random noise
(e.g., due to digital truncations of analog activations). Neural nets can nonetheless approximate large families of continuous maps, including chaotic maps and maps sensitive to initial conditions. A precise characterization of what maps can be approximated faulttolerantly by analog and discrete neural networks for unboundedly many iterations remains an open problem.
Journal:
Neurocomputing  IJON
, vol. 29, no. 13, pp. 2546, 1999
DOI:
10.1016/S09252312(99)001149
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Citation Context
(6)
...Our main conceptual theme is that of approximation and computability in dynamical systems from the theoretical viewpoint, and in spirit is somewhat akin to the work of Garzon, Botelho and Moore, see for example [
11
,24]...
Anthony Karel Seda
.
On the Integration of Connectionist and LogicBased Systems
...Furthermore, recurrent neural networks provide universal identification models in the restricted sense that they can approximate uniformly any MIMO nonlinear dynamic system over a finitetime interval, for every continuous and bounded input signal [6]–[
10
]...
P. Gil
,
et al.
On StateSpace Neural Networks for Systems Identification: Stability a...
...Recurrent neural networks topologies provide universal identification models in the restricted sense that they can approximate uniformly any MIMO nonlinear dynamic system over finitetime intervals for every continuous and bounded input signal [12]–[
16
]...
P. Gil
,
et al.
Order estimation in affine statespace neural networks
...[6]). Though neural networks are universal approximators [7], [
8
], they are quite dependent on the quality of the data set...
P. Gil
,
et al.
Constrained neural model predictive control with guaranteed free offse...
...Despite neural networks are well known universal approximators [10], [
11
], they are quite dependent on the quality of the data set...
P. Gil
,
et al.
Extended Neural Model Predictive Control of NonLinear Systems
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Citations
(10)
Approximation Capability of a Novel Neural Network Model for Dynamic Systems
Jianhai Zhang
,
WanZeng Kong
,
Senlin Zhang
,
Meiqin Liu
Conference:
International Conference on Intelligent Computation Technology and Automation  ICICTA
, 2009
Approximation of statespace trajectories by locally recurrent globally feedforward neural networks
(
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On the Integration of Connectionist and LogicBased Systems
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Anthony Karel Seda
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P. Gil
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Published in 2006.