Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/38990
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dc.contributor.authorJiao, Zhi Huaen_US
dc.date.accessioned2010-05-21T03:39:00Z
dc.date.available2010-05-21T03:39:00Z
dc.date.copyright1997
dc.date.issued1997
dc.identifier.urihttp://hdl.handle.net/10356/38990
dc.description.abstractIn heavy traffic, the Carrier Sense Multiple Access with Collision Detection (CSMA/CD) protocol suffers from numerous packets collisions resulting in a degradation of perfor-mance. A modified p—persistent CSMA/CD protocol(MP-CSMA/CD) has been proposed earlier which aims to maximize throughput performance. In this project, an artificial Neu-ral Network(NN) is utilized to optimize the MP-CSMA/CD protocol. The effects of neural network configurations and training parameters including learning rate, momentum and hidden neurons on neural network training are investigated. The simulation results show that the general throughput performance of neural network controlled MP-CSMA/CD local area network is better than that of CSMA/CD. In addition, the performance of the MP-CSMA/CD(NN) protocol under different load distributions (Even or Uneven load) is investigated. Some distribution functions are used to distribute the traffic along the bus to simulate actual traffic in the LAN. To ascertain the feasible implementation of this protocol, the effects of packet propagation delay are examined. Packet propagation delays may result in a drift in the probability of transmission due to the difference in sampled throughputs at different stations. Our simulations show that the trained neural network is insensitive to this noise in the sampled throughput and is able to steer the probability p in even or uneven load.en_US
dc.format.extent133 p.
dc.rightsNanyang Technological Universityen_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
dc.titleThe application of artificial neural network for optimization of MP-CSMA/CD protocolen_US
dc.typeThesisen_US
dc.contributor.supervisorSiew, David Chee Kheongen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMaster of Engineeringen_US
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