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- W2038768388 abstract "The Random Neural Network (RNN) is a recurrent neural network in which neurons interact with each other by exchanging excitatory and inhibitory spiking signals. The stochastic excitatory and inhibitory interactions in the network make the RNN an excellent modeling tool for various interacting entities. It has been applied in a number of applications such as optimization, image processing, communication systems, simulation pattern recognition and classification. In this paper, we briefly describe the RNN model and some learning algorithms for RNN. We discuss how the RNN with reinforcement learning was successfully applied to Cognitive Packet Network (CPN) architecture so as to offer users QoS driven packet delivery services. The experiments conducted on a 26-node testbed clearly demonstrated the learning capability of the RNNs in CPN." @default.
- W2038768388 created "2016-06-24" @default.
- W2038768388 creator A5080507159 @default.
- W2038768388 date "2013-07-01" @default.
- W2038768388 modified "2023-09-26" @default.
- W2038768388 title "The Random Neural Network and its learning process in Cognitive Packet Networks" @default.
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- W2038768388 doi "https://doi.org/10.1109/icnc.2013.6817951" @default.
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