Displaying similar documents to “Time-delay polynomial networks and rates of approximation.”

Time-varying time-delay estimation for nonlinear systems using neural networks

Yonghong Tan (2004)

International Journal of Applied Mathematics and Computer Science

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Nonlinear dynamic processes with time-varying time delays can often be encountered in industry. Time-delay estimation for nonlinear dynamic systems with time-varying time delays is an important issue for system identification. In order to estimate the dynamics of a process, a dynamic neural network with an external recurrent structure is applied in the modeling procedure. In the case where a delay is time varying, a useful way is to develop on-line time-delay estimation mechanisms to...

Noise Shaping in Neural Populations with Global Delayed Feedback

O. Ávila Åkerberg, M. J. Chacron (2010)

Mathematical Modelling of Natural Phenomena

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The interplay between intrinsic and network dynamics has been the focus of many investigations. Here we use a combination of theoretical and numerical approaches to study the effects of delayed global feedback on the information transmission properties of neural networks. Specifically, we compare networks of neurons that display intrinsic interspike interval correlations (nonrenewal) to networks that do not (renewal). We find that excitatory...

Neural network-based MRAC control of dynamic nonlinear systems

Ghania Debbache, Abdelhak Bennia, Noureddine Golea (2006)

International Journal of Applied Mathematics and Computer Science

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This paper presents direct model reference adaptive control for a class of nonlinear systems with unknown nonlinearities. The model following conditions are assured by using adaptive neural networks as the nonlinear state feedback controller. Both full state information and observer-based schemes are investigated. All the signals in the closed loop are guaranteed to be bounded and the system state is proven to converge to a small neighborhood of the reference model state. It is also...

Function approximation of Seidel aberrations by a neural network

Rossella Cancelliere, Mario Gai (2004)

Bollettino dell'Unione Matematica Italiana

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This paper deals with the possibility of using a feedforward neural network to test the discrepancies between a real astronomical image and a predefined template. This task can be accomplished thanks to the capability of neural networks to solve a nonlinear approximation problem, i.e. to construct an hypersurface that approximates a given set of scattered data couples. Images are encoded associating each of them with some conveniently chosen statistical moments, evaluated along the x , y ...

Hausdorff Approximation of Functions Different from Zero at One Point - Implementation in Programming Environment Mathematica

Kyurkchiev, Nikolay, Andreev, Andrey (2013)

Serdica Journal of Computing

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ACM Computing Classification System (1998): G.1.2. Moduli for numerical finding of the polynomial of the best Hausdorff approximation of the functions which differs from zero at just one point or having one jump and partially constant in programming environment MATHEMATICA are proposed. They are tested for practically important functions and the results are graphically illustrated. These moduli can be used for scientific research as well in teaching process of Approximation...