Learning in the recurrent random neural network
Neural Computation
Measurement and performance of a cognitive packet network
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue on networking middleware: selected papers from the TERENA networking conference 2001
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
Autonomous Smart Routing for Network QoS
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
Demonstrating cognitive packet network resilience to worm attacks
Proceedings of the 17th ACM conference on Computer and communications security
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 Random Neural Network (RNN) has been used in a wide variety of applications, including image compression, texture generation, pattern recognition, and so on. Our work focuses on the use of the RNN as a routing decision maker which uses Reinforcement Learning (RL) techniques to explore a search space (i.e. the set of all possible routes) to find the optimal route in terms of the Quality of Service metrics that are most important to the underlying traffic. We have termed this algorithm as the Cognitive Packet Network (CPN), and have shown in previous works its application to a variety of network domains. In this paper, we present a set of experiments which demonstrate how CPN performs in a realistic environment compared to a priori-computed optimal routes. We show that RNN with RL can autonomously learn the best route in the network simply through exploration in a very short time-frame. We also demonstrate the quickness with which our algorithm is able to adapt to a disruption along its current route, switching to the new optimal route in the network. These results serve as strong evidence for the benefits of the RNN Reinforcement Learning algorithm which we employ.