Name
Abstract | ||
|---|---|---|
In order to exploit the nonlinear time-varying property of network traffic, the traffic flow from controlled sources is described by a fuzzy autoregressive moving-average model with auxiliary input (fuzzy ARMAX process), with the traffic flow from uncontrolled sources (i.e., cross traffic) being described as external disturbances. In order to overcome the difficulty of the transmission delay in the design of congestion control, the fuzzy traffic model is translated to an equivalent fuzzy predictive traffic model. A fuzzy adaptive flow control scheme is proposed to avoid congestion at high utilization while maintaining good quality of service. By use of fuzzy adaptive prediction technique, the difficulties in congestion control design due to nonlinearity, time-varying characteristics, and large propagation delay can be overcome by the proposed adaptive traffic control method. A comparative evaluation is also given to show the superiority of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1109/TFUZZ.2003.814860 | IEEE T. Fuzzy Systems |
Keywords | Field | DocType |
traffic flow,fuzzy adaptive prediction technique,proposed adaptive traffic control,fuzzy autoregressive moving-average model,cross traffic,fuzzy adaptive predictive flow,fuzzy traffic model,fuzzy armax process,equivalent fuzzy predictive traffic,network traffic,atm network traffic,fuzzy adaptive flow control,propagation delay,prediction model,indexing terms,atm,flow control,moving average,asynchronous transfer mode,quality of service,fuzzy control,adaptive control,fuzzy system,congestion control,predictive control | Traffic generation model,Traffic flow,Control theory,Fuzzy logic,Flow control (data),Adaptive neuro fuzzy inference system,Adaptive traffic control,Fuzzy control system,Network traffic control,Mathematics | Journal |
Volume | Issue | ISSN |
11 | 4 | 1063-6706 |
Citations | PageRank | References |
23 | 1.15 | 21 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bor-Sen Chen | 1 | 2640 | 228.84 |
| Yu-Shuang Yang | 2 | 23 | 1.15 |
| Bore-Kuen Lee | 3 | 87 | 11.30 |
| Tsern-Huei Lee | 4 | 244 | 30.63 |