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Estimating Loss Rates in an Integrated Services Networkby Neural Networks
| Content Provider | Semantic Scholar |
|---|---|
| Author | Tong, Hui Brown, Timothy X. |
| Copyright Year | 1998 |
| Abstract | Loss rate is one of the most important Quality of Service (QoS) requirements in a packet communication network carrying multimedia traac. This paper presents a method for estimating loss rates as a function of a feature vector, x, based on a maximum likelihood principle. Two backpropagation networks, a multi-layer perceptron (MLP) network and a radial basis function (RBF) network, are applied to samples of simulated M/M/1 queue data, as well as to samples of simulated combinations of constant bit rate (CBR) and Poisson traac data, at diierent x. Our results show that a) this method is superior to earlier methods since it properly treats the samples, eliminating nuisance parameters, b) it gives a direct measure of QoS that can be used in higher level communication network decision functions, and c) RBF networks give more accurate loss rate estimates than MLP networks do. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://schof.colorado.edu/~timxb/timxb/publications/GCom98.ps |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Artificial neural network Backpropagation Carrying Emoticon Estimated Feature vector Hearing Loss, High-Frequency Integrated services Memory-level parallelism Multilayer perceptron Multimedia Network packet Neural Tube Defects Numerous Quad Flat No-leads package Quality of service Radial (radio) Radial basis function network Requirement Sensorineural Hearing Loss (disorder) Telecommunications network |
| Content Type | Text |
| Resource Type | Article |
