Recurrent neural network inference of internal delays in nonstationary data network

Authors:
Feng Qian;Guang-min Hu;Xing-miao Yao;Le-min Li
Affiliations:
Key Lab of Broadband Optical Fiber Transmission and Communication Networks, University of Electronic Science and Technology of China, Chengdu, China;Key Lab of Broadband Optical Fiber Transmission and Communication Networks, University of Electronic Science and Technology of China, Chengdu, China;Key Lab of Broadband Optical Fiber Transmission and Communication Networks, University of Electronic Science and Technology of China, Chengdu, China;Key Lab of Broadband Optical Fiber Transmission and Communication Networks, University of Electronic Science and Technology of China, Chengdu, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Year:
2006

Citing 3
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Sequential Monte Carlo inference of internal delays innonstationary data networks

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Abstract

By applying tomography theory which is highly developed in fieldssuchas medical computerized tomography and seismic tomography to communication network, network tomography has become one of the focused new technologies, which can infer the internal performance of the network by external end-to-end measurement. In this paper, we propose a novel Inference algorithm based on the recurrent multilayer perceptron (RMLP) network capable of tracking nonstationary network behavior and estimating time-varying, internal delay characteristics. Simulation experiments demonstrate the performance of the RMLP network.