A New Discrete-Time Multi-Constrained K -Winner-Take-All Recurrent Network and Its Application to Prioritized Scheduling

In this paper, we propose a novel discrete-time recurrent neural network aiming to resolve a new class of multi-constrained K-winner-take-all (K-WTA) problems. By facilitating specially designed asymmetric neuron weights, the proposed model is capable of operating in a fully parallel manner, thereby...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems Jg. 28; H. 11; S. 2674 - 2685
1. Verfasser:	Tien, Po-Lung
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.11.2017 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">K -winner take all Biological neural networks Computational modeling Computer simulation Convergence Data processing Energy consumption Energy saving Latency Neural networks Neurons parallel computation prioritized scheduling Quality of service quality of service (QoS) Radio frequency recurrent neural network Recurrent neural networks Routing Scheduling Take-all disease
ISSN:	2162-237X, 2162-2388, 2162-2388
Online-Zugang:	Volltext
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