Methodology based on spiking neural networks for univariate time-series forecasting

Lucas, Sergio; Portillo, Eva

doi:10.1016/j.neunet.2024.106171

Cited by 6 publications

(1 citation statement)

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“…Due to the large fluctuations in the numerical characteristics of the network flow subsequences, it is difficult to adapt the fixed patterns of statistical features to all types of subsequences. In terms of dictionary pattern-based classification, Lin J, Radford A, and He K, respectively, proposed a pattern packet algorithm [15], a symbol aggregation approximation algorithm [16], and a time series classification based on a word extraction algorithm [17]. These algorithms convert time series data into pattern packets and distinguish subsequence categories based on the relative frequency of a pattern packet's appearance.…”

Section: Introductionmentioning

confidence: 99%

Research on Fault Detection by Flow Sequence for Industrial Internet of Things in Sewage Treatment Plant Case

Lei,

Zhao,

Chen

2024

Sensors

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Classifying the flow subsequences of sensor networks is an effective way for fault detection in the Industrial Internet of Things (IIoT). Traditional fault detection algorithms identify exceptions by a single abnormal dataset and do not pay attention to the factors such as electromagnetic interference, network delay, sensor sample delay, and so on. This paper focuses on fault detection by continuous abnormal points. We proposed a fault detection algorithm within the module of sequence state generated by unsupervised learning (SSGBUL) and the module of integrated encoding sequence classification (IESC). Firstly, we built a network module based on unsupervised learning to encode the flow sequence of the different network cards in the IIoT gateway, and then combined the multiple code sequences into one integrated sequence. Next, we classified the integrated sequence by comparing the integrated sequence with the encoding fault type. The results obtained from the three IIoT datasets of a sewage treatment plant show that the accuracy of the SSGBUL–IESC algorithm exceeds 90% with subsequence length 10, which is significantly higher than the accuracies of the dynamic time warping (DTW) algorithm and the time series forest (TSF) algorithm. The proposed algorithm reaches the classification requirements for fault detection for the IIoT.

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Section: Introductionmentioning

confidence: 99%