Design a Hybrid Framework for Air Pollution Forecasting

Lin, Chi-Yeh; Chang, Yue-Shan; Chiao, Hsin-Ta; Abimannan, Satheesh

doi:10.1109/smc.2019.8914257

Cited by 3 publications

(5 citation statements)

References 25 publications

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“…The dissipation properties of chaotic systems are one of the most important issues to consider in the dynamic analysis of chaotic systems. As shown in [48], the dissipation properties of a chaotic system's convergence region or domain of attraction are contracted. As is well known, the divergence of a chaotic system's vector field is given by [48] ∇.F = ∂F ∂x 1…”

Section: Related Workmentioning

confidence: 99%

“…As shown in [48], the dissipation properties of a chaotic system's convergence region or domain of attraction are contracted. As is well known, the divergence of a chaotic system's vector field is given by [48] ∇.F = ∂F ∂x 1…”

Section: Related Workmentioning

confidence: 99%

“…, ( 29), (32), (35), (37), (48). (49) Equation ( 49) contains nonlinear constraints (Equations ( 26), ( 29), (32), (35), (37) and ( 48)).…”

Section: Conflicts Of Interestmentioning

confidence: 99%

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Complex Dynamic System Modelling, Identification and Control

2023

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Complex Dynamic System Modelling, Identification and Control

2023

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“…Jing et al [ 47 ] selected the characteristic sub-sequence by PCC to improve the prediction accuracy when forecasting photo-voltaic power output. Lin et al [ 48 ] built a hybrid model framework using the stacking scheme of integrated learning by PCC between different models. As for the prediction problem, PCC requires a known prediction target variable, so it cannot be applied for predicting.…”

Section: Related Workmentioning

confidence: 99%

“…(1) A series causality entropy method is developed to select the related input data for the neural network. Compared with the PCC [ 48 ], Spearman correlation [ 45 ], and the Kendall correlation coefficient method [ 61 ], the method does not depend on the prediction results and is suitable for prediction problems based on measurement data in multi-sensor systems.…”

Section: Related Workmentioning

confidence: 99%

Distributed Deep Fusion Predictor for a Multi-Sensor System Based on Causality Entropy

Jin

et al. 2021

Entropy

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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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