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Your search keyword '"Zhou, Zhi-Jie"' showing total 2 results
Start Over Author "Zhou, Zhi-Jie" Topic evidential reasoning Topic prediction models
2 results on '"Zhou, Zhi-Jie"'

1. A new hidden behavior prediction model of complex systems under perturbations.

Author

Zhou, Zhi-Jie, Tang, Shuai-Wen, Hu, Chang-Hua, Cao, You, Han, Xiao-Xia, and Ning, Peng-Yun

Subjects
*PREDICTION models, *BEHAVIORAL research, *KNOWLEDGE transfer
Abstract

It is vital to predicting the hidden behavior of complex systems, which is associated with the operation state of the system and cannot be directly observed. In recent research of hidden behavior prediction, one effective method called the semi-quantitative information-based method has received wide applications and become the main stream, where both the qualitative knowledge and the quantitative data are involved. Although some methods have been effectively applied to predict the hidden behavior, only the single behavior prediction has been achieved. It is shown that multiple hidden behaviors are not considered, which may not meet actual requirements of the system. Besides, the influence of perturbations on the system is not considered, which can indirectly affect the system's hidden behavior and reduce the hidden behavior prediction accuracy. As such, a new hidden behavior prediction model based on the evidential reasoning (ER) rule considering perturbations is proposed in this paper, which is typically a semi-quantitative information-based method and can deal with multiple hidden behaviors. A parameter estimation algorithm is proposed based on the expectation maximization (EM) to obtain more accurate model parameters. A case study is conducted to verify the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Published
2022
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2. New model for system behavior prediction based on belief rule based systems

Author

Zhou, Zhi-Jie, Hu, Chang-Hua, Xu, Dong-Ling, Yang, Jian-Bo, and Zhou, Dong-Hua

Subjects
*PREDICTION models, *COMPLEXITY (Philosophy), *ELECTRONIC data processing, *QUALITATIVE research, *EXPERT systems, *INFORMATION theory, *EXPECTATION-maximization algorithms, *ALGORITHMS, *NUMERICAL analysis, *SIMULATION methods & models
Abstract

Abstract: To predict the behavior of a complex engineering system, a model can be built and trained using historical data. However, it may be difficult to obtain a complete and accurate set of data to train the model. Consequently, the model may be incapable of predicting the future behavior of the system with reasonable accuracy. On the other hand, expert knowledge of a qualitative nature and partial historical information about system behavior may be available which can be converted into a belief rule base (BRB). Based on the unique features of BRB, this paper is devoted to overcoming the above mentioned difficulty by developing a forecasting model composed of two BRBs and two recursive learning algorithms, which operate together in an integrated manner. An initially constructed forecasting model has some unknown parameters which may be manually tuned and then trained or updated using the learning algorithms once data become available. Based on expert intervention which can reflect system operation patterns, two algorithms are developed on the basis of the evidential reasoning (ER) algorithm and the recursive expectation maximization (EM) algorithm with the former used for handling judgmental outputs and the latter for processing numerical outputs, respectively. Using the proposed algorithms, the training of the forecasting model can be started as soon as there are some data available, without having to wait until a complete set of data are all collected, which is critical when the forecasting model needs to be updated in real-time within a given time limit. A numerical simulation study shows that under expert intervention, the forecasting model is flexible, can be automatically tuned to predict the behavior of a complicated system, and may be applied widely in engineering. It is demonstrated that if certain conditions are met, the proposed recursive algorithms can converge to a local optimum. A case study is also conducted to show the wide potential applications of the forecasting model. [Copyright &y& Elsevier]

Published
2010
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