+ Site Statistics
+ Search Articles
+ Subscribe to Site Feeds
EurekaMag Most Shared ContentMost Shared
EurekaMag PDF Full Text ContentPDF Full Text
+ PDF Full Text
Request PDF Full TextRequest PDF Full Text
+ Follow Us
Follow on FacebookFollow on Facebook
Follow on TwitterFollow on Twitter
Follow on Google+Follow on Google+
Follow on LinkedInFollow on LinkedIn

+ Translate

Neural approximation of open-loop feedback rate control in satellite networks

, : Neural approximation of open-loop feedback rate control in satellite networks. IEEE Transactions on Neural Networks 16(5): 1195-1211

A resource allocation problem for a satellite network is considered, where variations of fading conditions are added to those of traffic load. Since the capacity of the system is finite and divided in finite discrete portions, the resource allocation problem reveals to be a discrete stochastic programming one, which is typically NP-Hard. In practice, a good approximation of the optimal solution could be obtained through the adoption of a closed-form expression of the performance measure in steady-state conditions. Once we have summarized the drawbacks of such optimization strategy, we address two novel optimization approaches. The first one derives from Gokbayrak and Cassandras and is based on the minimization over the discrete constraint set using an estimate of the gradient, obtained through a "relaxed continuous extension" of the performance measure. The computation of the gradient estimation is based on infinitesimal perturbation analysis (IPA). Neither closed forms of the performance measures, nor additional feedbacks concerning the state of the system and very mild assumptions about the stochastic environment are requested. The second one is the main contribution of the present work, and is based on an open-loop feedback control (OLFC) strategy, aimed at providing optimal reallocation strategies as functions of the state of the network. The optimization approach leads us to a functional optimization problem, and we investigate the adoption of a neural network-based technique, in order to approximate its solution. As is shown in the simulation results, we obtain near-optimal reallocation strategies with a small real time computational effort and avoid the suboptimal transient periods introduced by the IPA gradient descent algorithm.

Accession: 049668872

PMID: 16252826

DOI: 10.1109/TNN.2005.853424

Submit PDF Full Text: Here

Submit PDF Full Text

No spam - Every submission is manually reviewed

Due to poor quality, we do not accept files from Researchgate

Submitted PDF Full Texts will always be free for everyone
(We only charge for PDFs that we need to acquire)

Select a PDF file:

Related references

Lin, S.; Cao, F.; Xu, Z., 2011: Essential rate for approximation by spherical neural networks. We consider the optimal rate of approximation by single hidden feed-forward neural networks on the unit sphere. It is proved that there exists a neural network with n neurons, and an analytic, strictly increasing, sigmoidal activation function suc...

Balavoine, Aèle.; Romberg, J.; Rozell, C.J., 2015: Convergence and rate analysis of neural networks for sparse approximation. We present an analysis of the Locally Competitive Algorithm (LCA), which is a Hopfield-style neural network that efficiently solves sparse approximation problems (e.g., approximating a vector from a dictionary using just a few nonzero coefficients...

Osborn, J.; D.C.s Juez, F.Javier.; Guzman, D.; Butterley, T.; Myers, R.; Guesalaga, Aés.; Laine, J., 2012: Using artificial neural networks for open-loop tomography. Modern adaptive optics (AO) systems for large telescopes require tomographic techniques to reconstruct the phase aberrations induced by the turbulent atmosphere along a line of sight to a target which is angularly separated from the guide sources...

Chou, Z.; Lim, J.; Brown, S.; Keller, M.; Bugbee, J.; Broccard, Fédéric..D.; Khraiche, M.L.; Silva, G.A.; Cauwenberghs, G., 2016: Bidirectional neural interface: Closed-loop feedback control for hybrid neural systems. Closed-loop neural prostheses enable bidirectional communication between the biological and artificial components of a hybrid system. However, a major challenge in this field is the limited understanding of how these components, the two separate n...

Vaitl D.; Pepping G.; Lichter K.; Kenkmann H.J., 1977: Compensatory heart rate changes during an open loop feedback procedure. Psychophysiology 14(1): 107

Tiwari, A.; Igoshin, O.A., 2013: Coupling between feedback loops in autoregulatory networks affects bistability range, open-loop gain and switching times. Biochemical regulatory networks governing diverse cellular processes such as stress-response, differentiation and cell cycle often contain coupled feedback loops. We aim at understanding how features of feedback architecture, such as the number of...

Liu, Y-Jun.; Li, S.; Tong, S.; Chen, C.L.Philip., 2016: Neural Approximation-Based Adaptive Control for a Class of Nonlinear Nonstrict Feedback Discrete-Time Systems. NlmCategory="UNASSIGNED">In this paper, an adaptive control approach-based neural approximation is developed for a class of uncertain nonlinear discrete-time (DT) systems. The main characteristic of the considered systems is that they...

Wylie R.M., 1984: Visual tracking by monkeys feedback control or open loop. Society for Neuroscience Abstracts 10(1): 341

Dierks, T.; Jagannathan, S., 2010: Output feedback control of a quadrotor UAV using neural networks. In this paper, a new nonlinear controller for a quadrotor unmanned aerial vehicle (UAV) is proposed using neural networks (NNs) and output feedback. The assumption on the availability of UAV dynamics is not always practical, especially in an outdo...

Radil Weiss T.; Spunda F., 1972: Control of the hippocampal theta rhythm in open loop and feedback conditions. Physiologia Bohemoslovaca 21(4): 429-430