Document Type


Date of Award


Degree Name

Master of Science in Electrical Engineering - (M.S.)


Electrical and Computer Engineering

First Advisor

Edwin Hou

Second Advisor

MengChu Zhou

Third Advisor

John D. Carpinelli


In this thesis, we develop a neural network method based on Adaptive Resonance Theory to train and control the optimum bandwidth allocation of ATM network. In Broadband Integrated Service Digital Network (BISDN), the Asynchronous Transfer Mode (ATM) is already adopted as the transfer facility by CCITT. ATM is a high-bandwidth, low- delay, fast-packet switching and multiplexing technique. Using ATM technique, we can flexibly rearrange the network and reassign the bandwidth to meet the requirement of all types of services. As an effective optimization method, Genetic Algorithm (GA) is applied to implement the bandwidth allocation of ATM. Then, we use Adaptive Resonance Theory (ART) and simulation results to build a weight table. By using this table, we can immediately find the best bandwidth allocation of ATM network when there is a set of input traffic. That is to say, we can control the bandwidth allocation output by using these weight values. Finally, we analyze the computer simulation results in this work.



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