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Sensor exception detection method based on regularized vector autoregression model

An autoregressive model, anomaly detection technology, applied in instruments, character and pattern recognition, complex mathematical operations, etc., can solve problems such as the decline of prediction ability, and achieve the effect of reducing complexity

Inactive Publication Date: 2016-07-06
TIANJIN UNIV
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Problems solved by technology

However, in many practical applications, such as sensor data, the amount of data is relatively large, and the predictive ability of this method will decrease

Method used

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  • Sensor exception detection method based on regularized vector autoregression model
  • Sensor exception detection method based on regularized vector autoregression model
  • Sensor exception detection method based on regularized vector autoregression model

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Embodiment Construction

[0019] Considering the geometric structure between the data, when constructing the regularization constraint item, the idea based on the flow assumption is used for reference: if two data points have similar geometric distributions in the high-dimensional space, the two data points in the resulting space after dimensionality reduction The data points should also be similar, and the flow assumption plays an important role in the data dimensionality reduction algorithm. When the data flow is unknown, the nearest neighbor graph of the data point can be used to approximate, so that the corresponding constraint item can be constructed considering the nearest neighbor graph of the data point. If a graph consists of n vertices, where each vertex corresponds to a data point, and considering the relationship between fixed points and fixed points, the weight matrix W of the defined edge is as follows:

[0020]

[0021] where N p (x i ) means x i A data set consisting of the p near...

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Abstract

The invention relates to a sensor exception detection method based on a regularized vector autoregression model. The sensor exception detection method comprises the following steps: (1) establishing a multielement linear regression model, and determining a target function; collecting data by a sensor; establishing a nearest neighbor graph of data points, wherein the graph consists of n vertexes, each vertex corresponds to one data point, and the weight matrix of edges is defined by considering a relationship between fixed points; constructing a bound term in order to keep similarity among original data points while the obstacles of high dimension and overfitting can be overcome; and utilizing the target function to train a model parameter to obtain an optimal parameter coefficient, and utilizing the model obtained by training to carry out exception detection. The sensor exception detection method can better predict original data.

Description

technical field [0001] The invention belongs to the technical field of sensor anomaly detection, in particular to the sensor anomaly detection technology. Background technique [0002] Anomaly detection refers to the detection and discovery of data patterns that do not conform to normal expected behavior in observed sample data. It is one of the very active topics in data mining research, occupies a leading position, and is widely used. The application of anomaly detection technology can effectively prevent network intrusion, ensure the safety of industrial production, and monitor equipment failures. Active and popular research discipline. Anomaly detection is a very special task. This is mainly because most of the real data only have data patterns that meet the expected (normal class) behavior, while rare or unknown data patterns that violate the expected (abnormal class) behavior. The extreme imbalance of observation samples (the number of abnormal samples is much smalle...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/18G06K9/62
CPCG06F17/18G06F18/24147
Inventor 韦义明何改云王建
Owner TIANJIN UNIV
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