Intelligent dynamic spectrum access using hybrid genetic operators

Islam, Md. Jahidul; Islam, Md. Monirul; Islam, Aminul

doi:10.1016/j.swevo.2017.03.004

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“…This is shown in Equation 7, where Equation 8 shows the a squashing function used on the previous membrane voltage v t . Other implementations of MPD's for reinforcement learning have also been conducted recently [36] [37].…”

Section: B Snnsmentioning

confidence: 99%

Topological Evolution of Spiking Neural Networks

Slade

Zhang

2018

2018 International Joint Conference on Neural Networks (IJCNN)

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Neuro-evolution is often used to generate the parameters, topology, and rules of artificial neural networks. This technique allows for automatic configuration of a neural network. In this paper we propose a method to generate Spiking Neural Networks (SNNs) automatically called NENG (Neuro-Evolutionary Network Generation). The aim was to help alleviate the manual construction and optimization of neural network implementations. The results show the algorithm is successful at generating and improving the design of SNNs for a Classification task. After 812 generations with a population size of 20 the algorithm converges to model the Xor gate with 100% accuracy. The results show improvements to the algorithm execution time and number of neurons over time.

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Section: B Snnsmentioning

confidence: 99%