Traffic models in broadband networks
IEEE Communications Magazine
Multimedia traffic characteristics in broadband networks
IEEE Communications Magazine
Guest editorial: intelligent techniques in high speed networks
IEEE Journal on Selected Areas in Communications
The superposition of variable bit rate sources in an ATM multiplexer
IEEE Journal on Selected Areas in Communications
Characterizing Superposition Arrival Processes in Packet Multiplexers for Voice and Data
IEEE Journal on Selected Areas in Communications
Equivalent capacity and its application to bandwidth allocation in high-speed networks
IEEE Journal on Selected Areas in Communications
A knowledge-base generating hierarchical fuzzy-neural controller
IEEE Transactions on Neural Networks
Learning and tuning fuzzy logic controllers through reinforcements
IEEE Transactions on Neural Networks
On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm
IEEE Transactions on Neural Networks
Fuzzy multi-layer perceptron, inferencing and rule generation
IEEE Transactions on Neural Networks
VHDL implementation of neuro-fuzzy based adaptive bandwidth controller for ATM networks
International Journal of Communication Networks and Distributed Systems
A Q-learning model-independent flow controller for high-speed networks
ACC'09 Proceedings of the 2009 conference on American Control Conference
Nash Q-learning multi-agent flow control for high-speed networks
ACC'09 Proceedings of the 2009 conference on American Control Conference
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In this work, a hierarchical neuro-fuzzy call admission controller for ATM networks based on the GARIC architecture is proposed. The controller contains a neural network as a critic, using the reinforcement learning scheme, and three fuzzy sub-networks, controlling cell loss ratio, queue size and link utilization in the ATM multiplexer. The final decision of the call admission controller is obtained as a weighted combination of the decisions generated by the fuzzy sub-networks. In order to study the performance of the proposed controller, it is simulated under various variable bit rate traffic patterns and the results obtained are evaluated in terms of network utilization. Introduction of an initial knowledge base to improve training times is discussed and the results with and without the knowledge base are given. Finally, methods to enhance the performance of the proposed controller are mentioned.