AnRAD: A Neuromorphic Anomaly Detection Framework for Massive Concurrent Data Streams

The evolution of high performance computing technologies has enabled the large-scale implementation of neuromorphic models and pushed the research in computational intelligence into a new era. Among the machine learning applications, unsupervised detection of anomalous streams is especially challeng...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems 2018-05, Vol.29 (5), p.1622-1636
Hauptverfasser:	Chen, Qiuwen, Luley, Ryan, Wu, Qing, Bishop, Morgan, Linderman, Richard W., Qiu, Qinru
Format:	Artikel
Sprache:	eng
Schlagworte:	Anomalies Anomaly detection Computational modeling Computational neuroscience Computer architecture Computer memory Computer simulation Computing time Data transmission Feature extraction Firing pattern general purpose graphics processing unit (GPGPU) heterogeneous systems Intelligence Knowledge bases (artificial intelligence) Learning algorithms Machine learning Memory Neural networks neuromorphic computing Neuromorphics Neurons Real time Real-time systems Testing
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