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One major problem in the management of the current large networks is the complexity and the enormous amount of operations required to satisfy user demands while using resources efficiently. In this study, we propose a network traffic forecasting strategy based on BP neural network (BP-NTF). First, we analyse the characteristics of network traffic and establish traffic forecasting methods based on...
We have developed a novel method to estimate missing observations in wireless sensor networks. We use a hierarchical unsupervised fuzzy ART neural network to represent the data cluster prototypes. We then estimate missing inputs by using a new spatial-temporal imputation technique. We have evaluated this approach through experiments on both real sensor data and artificially generated data. Our experimental...
In this paper, we present an anomaly detection system that is able to detect time-related anomalies by using a wireless sensor network and a mobile robot. The sensor network uses an unsupervised fuzzy adaptive resonance theory (ART) neural network to learn and detect intruders in a previously unknown environment. Upon the detection of an intruder, a mobile robot travels to the position where the intruder...
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