EurekaMag.com logo
+ Site Statistics
References:
52,725,316
Abstracts:
28,411,598
+ 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


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



Related references

Essential rate for approximation by spherical neural networks. Neural Networks 24(7): 752-758, 2011

Convergence and rate analysis of neural networks for sparse approximation. IEEE Transactions on Neural Networks and Learning Systems 23(9): 1377-1389, 2015

Using artificial neural networks for open-loop tomography. Optics Express 20(3): 2420-2434, 2012

Bidirectional neural interface: Closed-loop feedback control for hybrid neural systems. Conference Proceedings 2015: 3949-3952, 2016

Compensatory heart rate changes during an open loop feedback procedure. Psychophysiology 14(1): 107, 1977

Coupling between feedback loops in autoregulatory networks affects bistability range, open-loop gain and switching times. Physical Biology 9(5): 055003-055003, 2013

Neural Approximation-Based Adaptive Control for a Class of Nonlinear Nonstrict Feedback Discrete-Time Systems. IEEE Transactions on Neural Networks and Learning Systems (): -, 2016

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

Output feedback control of a quadrotor UAV using neural networks. IEEE Transactions on Neural Networks 21(1): 50-66, 2010

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