Abstract
When the immune network is used in anomalistic electromagnetism signals detection, a critical issue emerged that lots of “detection holes (gaps of the detector coverage compare to the target area)” are caused by fixed-size detectors and invariable matching threshold. This paper improves the original model by increasing the detectors’ coverage and proposes an anomalistic electromagnetism signal detection model based on an immune network with variable any-r-intervals matching rule. The sizes and matching thresholds of different detectors will learn from the training set in this model, which can reduce the detection holes obviously. The model evaluated by wireless signals and the experiment results show that the model can reduce detection holes and improve detection accuracy compared with the original model.
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Feng, X., Sun, Z., Li, B. (2019). An Anomaly Detection Model Based on Immune Network with Variable Any-R-Intervals Matching Rule. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 515. Springer, Singapore. https://doi.org/10.1007/978-981-13-6264-4_45
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DOI: https://doi.org/10.1007/978-981-13-6264-4_45
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