Learning in the recurrent random neural network
Neural Computation
On the design of IP routers part 1: Router architectures
Journal of Systems Architecture: the EUROMICRO Journal
Design and performance of cognitive packet networks
Performance Evaluation
Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
FPGA and CPLD Architectures: A Tutorial
IEEE Design & Test
Cognitive Packet Networks: QoS and Performance
MASCOTS '02 Proceedings of the 10th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems
Design and implementation of a random neural network routing engine
IEEE Transactions on Neural Networks
IEEE Network: The Magazine of Global Internetworking
An initiative for a classified bibliography on G-networks
Performance Evaluation
Bibliography on G-networks, negative customers and applications
Mathematical and Computer Modelling: An International Journal
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The cognitive packet network (CPN) routing protocol provides a framework for real-time quality of service decision making within packet networks. This allows the paths taken by packets to autonomously adapt to changing conditions in order to maintain and improve on the quality of service provided by current routing algorithms. Software implementations of the protocol use the random neural network with reinforcement learning. This algorithm is unsuitable for implementation in dedicated hardware or devices with low computational abilities due to its complexity. We present a series of alternative algorithms for use in CPN, and compare their complexity and performance with respect to software and hardware implementation. Through experimentation we demonstrate that it is possible to match the performance of the random neural network with the simpler alternative algorithms. We also propose an architecture for an FPGA based hardware CPN router, and describe our implementation.