期刊
ENTROPY
卷 23, 期 2, 页码 -出版社
MDPI
DOI: 10.3390/e23020219
关键词
series causality analysis; Bayesian LSTM; multi-sensor system; meteorological data; big measurement data; deep fusion predictor
资金
- National Key Research and Development Program of China [2020YFC1606801]
- National Natural Science Foundation of China [61903009, 61903008]
- Beijing Municipal Education Commission [KM201910011010, KM201810011005]
- Young Teacher Research Foundation Project of BTBU [QNJJ2020-26]
- Defense Industrial Technology Development Program [6142006190201]
- Beijing excellent talent training support project for young top-notch team [2018000026833TD01]
This paper proposes a trend prediction method based on sensor data, which uses causality entropy and series causality coefficient to select high causal measurements as input data, and utilizes Bayesian method and multi-layer perceptron to build the prediction model. Experimental results demonstrate the proposed method can effectively enhance the prediction performance of multi-sensor systems.
Trend prediction based on sensor data in a multi-sensor system is an important topic. As the number of sensors increases, we can measure and store more and more data. However, the increase in data has not effectively improved prediction performance. This paper focuses on this problem and presents a distributed predictor that can overcome unrelated data and sensor noise: First, we define the causality entropy to calculate the measurement's causality. Then, the series causality coefficient (SCC) is proposed to select the high causal measurement as the input data. To overcome the traditional deep learning network's over-fitting to the sensor noise, the Bayesian method is used to obtain the weight distribution characteristics of the sub-predictor network. A multi-layer perceptron (MLP) is constructed as the fusion layer to fuse the results from different sub-predictors. The experiments were implemented to verify the effectiveness of the proposed method by meteorological data from Beijing. The results show that the proposed predictor can effectively model the multi-sensor system's big measurement data to improve prediction performance.
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