Dynamic Application Profiles using Neural Networks for adaptive quality of service support in the Internet

Tsetsekas, Charilaos A.; Fertis, Apostolos G.; Venieris, Iakovos S.

doi:10.1016/j.comcom.2006.04.007

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…In paper [9] is presented decentralized neuro-adaptive congestion control scheme comprising an on-line desired round-trip estimator and adaptive transmission rate controller. The paper [29] reports estimation of the traffic features such as peak rate, token bucket rate and token bucket size, produced by network applications. Three neural network types of estimators were tested: the feed-forward NN with backpropagation, the feed-forward NN with temporal learning and ANFIS.…”

Section: Related Workmentioning

confidence: 99%

On the application of fuzzy-based flow control approach to High Altitude Platform communications

2009

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Most of the research effort in the field of HAP communications until now has been invested in the physical layer of the protocol stack, and in the radio related issues in particular. However, the overall system throughput is limited by the performance of the transport layer. Since HAPs will be used in networks with different topological complexity, various kinds of wireless communications links, bit error rates, and various mixtures of multimedia traffic, the control flow in such networks may present itself as a non-linear and stochastic process. Therefore we introduced a fuzzy control of the throughput in the TCP. Our approach is based on the off-line synthesis of the Takagi-Sugeno fuzzy controller based on the simulation data and on-line flow control by the synthesized controller that is built in the conventional TCP. In the paper we present the ns2-based simulation results.

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Section: Related Workmentioning

confidence: 99%

On the application of fuzzy-based flow control approach to High Altitude Platform communications

2009

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Yapay Sinir Ağları Kullanılarak Konutlarda Elektrik Tüketimi Düzeylerinin Tahmin Edilmesi

ERCAN¹,

Irmak²,

Çevik³

et al. 2020

Sosyoekonomi

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Most of the studies on electricity consumption were conducted using econometric models and statistical methods. Studies that aiming at predicting electricity consumption levels using household characteristics and utilizing machine learning methods couldn't be found in the literature. This study is aiming at presenting a model proposal that predicts the electricity consumption levels in dwellings as lower consumption and higher consumption classes, using household and dwelling characteristics. Artificial Neural Networks were utilized as a machine learning method in modeling phase. Data were gathered from Turkish Statistical Institution's Household Budget Survey. The records having no electricity consumption were removed and mean electricity consumption was determined from remaining 32,765 households. Records above the mean were labelled as high-consumption class and that are below the mean were labelled as low-consumption class. ANN model training was carried out using 24,574 (70%) household data. Remaining 8,191 (30%) household data were used for testing the model. The success of the model was 75.11% at training phase, and it was 65.56% at testing phase. As a result, the model proposal predicting electricity consumption levels using household and dwelling characteristics to contribute electricity production and distribution planning is presented.

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Dynamic Application Profiles using Neural Networks for adaptive quality of service support in the Internet

Cited by 2 publications

References 19 publications

On the application of fuzzy-based flow control approach to High Altitude Platform communications

On the application of fuzzy-based flow control approach to High Altitude Platform communications

Yapay Sinir Ağları Kullanılarak Konutlarda Elektrik Tüketimi Düzeylerinin Tahmin Edilmesi

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