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With the development of artificial intelligence algorithm, BP neural network algorithm is widely used in many fields, such as fault diagnosis, intelligent control and dynamic signal processing, because it has many advantages for example self-learning, self-organization and nonlinear mapping. Compared with BP neural network, the hidden Markov model is suitable for dynamic time series modeling and has...
Outliers are observations that lie far away from the fitting function deduced from the bulk of a set of observations. The outlier detection has become more challenging when the nature of data has involved with the “concept drifting.” To address this challenging issue, this study explores a decision support mechanism (DSM) for coping with the outlier detection problem in the concept drifting environment...
Detecting anomaly behavior in large network traffic data has presented a great challenge in designing effective intrusion detection systems. We propose an adaptive model to learn majority patterns under a dynamic changing environment. We first propose unsupervised learning on data abstraction to extract essential features of samples. We then adopt incremental majority learning with iterative evolutions...
IP flow analysis is an effective way of doing network forensic analysis which aims to detect attack patterns and identify attackers in a given network traffic data. For attacks such as Distributed Denial of Service (DDoS), efficiently identifying the botnet in time can be a challenge. Recently, the unsupervised learning methods such as the K-means, self-organizing map (SOM), and growing hierarchical...
Network anomaly detection aims to detect patterns in a given network traffic data that do not conform to an established normal behavior. Distinguishing different anomaly patterns from large amount of data can be a challenge, let alone visualizing them in a comparative perspective. Recently, the unsupervised learning method such as the K-means [3], self-organizing map (SOM) [2], and growing hierarchical...
The purpose of this research is to investigate the relation between international stock markets and Taiwan's stock market, and use the statistical method to identify international markets of high correlation with Taiwan's stock market as the input parameters of the ANFIS (Adaptive Network-based Fuzzy Inference System) model to improve the forecasting accuracy. This paper collects dataset in the period...
Based on the samples of tropical cyclones in June-October from 2001-2010 over the western pacific sea. A nonlinear prediction models of tropical cyclones intensity has been presented by PSO-ANN method. It differs from traditional prediction modeling in the following aspects: (1)About the input factors of the PSO-ANN model, firstly using stepwise regression selected a combination of factors from 62...
In this paper, a novel neural network ensemble forecast model is developed where the stepwise regression method are chosen for forecast factors best correlated with the series of typhoon intensity, and the main information is extracted from remaining forecast factors where Locally Linear Embedding (LLE) method is used. Further the problem that network structure determination and network easily into...
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