Ensemble Belief Rule-Based Model for complex system classification and prediction

You, Yaqian; Sun, Jianbin; Chen, Yu‐Wang; Niu, Caiyun; Jiang, Jiang

doi:10.1016/j.eswa.2020.113952

Cited by 26 publications

(3 citation statements)

References 33 publications

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“…RUL prediction is a key determinate for maintenance scheduling and costing in CES [12,26]. Sensor-fed or manually recorded time series data is typically translated into a health index, the methods for which are dependent on the application [17,49,52,53]. Variants of RNN are widely used to predict RUL for CES, many using the C-MAPSS dataset.…”

Section: Research Gapsmentioning

confidence: 99%

Multistep prediction of dynamic uncertainty under limited data

Grenyer

Schwabe

Erkoyuncu

et al. 2022

CIRP Journal of Manufacturing Science and Technology

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Section: Research Gapsmentioning

confidence: 99%

Multistep prediction of dynamic uncertainty under limited data

Grenyer

Schwabe

Erkoyuncu

et al. 2022

CIRP Journal of Manufacturing Science and Technology

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“…Information from each model must be considered, and a combined model with appropriate weighting configuration must be constructed to improve forecasting accuracy and stability [14]. Currently, common approaches for combined model prediction include averaging methods [15], weighted averaging methods [16], and voting methods [17], which combine the predictions of individual models such as support vector machine regression (SVR) [18], artificial neural networks (ANN) [19], or long short-term memory (LSTM) [20]. However, combined forecasting models based on average weights or voting lack effective utilization of performance differences between forecasting models and may result in reduced forecasting accuracy when there are significant performance differences between the individual models [21].…”

Section: Introductionmentioning

confidence: 99%

Optimal Extreme Random Forest Ensemble for Active Distribution Network Forecasting-Aided State Estimation Based on Maximum Average Energy Concentration VMD State Decomposition

Guo

Jin

2023

Energies

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As the penetration rate of distributed generators (DG) in active distribution networks (ADNs) gradually increases, it is necessary to accurately estimate the operating state of the ADNs to ensure their safe and stable operation. However, the high randomness and volatility of distributed generator output and active loads have increased the difficulty of state estimation. To solve this problem, a method is proposed for forecasting-aided state estimation (FASE) in ADNs, which integrates the optimal extreme random forest based on the maximum average energy concentration (MAEC) and variable mode decomposition (VMD) of states. Firstly, a parameter optimization model based on MAEC is constructed to decompose the state variables of the ADNs into a set of intrinsic mode components using VMD. Then, strongly correlated weather and date features in ADNs state prediction are selected using the multivariate rapid maximum information coefficient (RapidMIC) based on Schmidt orthogonal decomposition. Finally, by combining the set of intrinsic mode functions of the ADNs state, calendar rules, and weather features, an ensemble FASE method based on the extreme random tree (ERT) ensemble for the ADNs based on cubature particle filtering (CPF) is developed. An optimization model based on mean absolute error and root mean square error is established to obtain the optimal integration strategy and final estimation results. Simulation verification is performed on the IEEE 118-bus standard distribution system. The results show that the proposed method achieves higher accuracy compared to other estimation methods, with root mean square errors of 1.4902 × 10−4 for voltage magnitude and 4.8915 × 10−3 for phase angle.

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“…Similar to FRB, BRB systems take the form of belief rules. Belief rules are based on traditional IF-THEN fuzzy rules but with belief degrees introduced to the consequent parts [35], and they provide an informative scheme of formulating expert experience, uncertain knowledge and hybrid information [36], [37]. BRB systems are capable to capture nonlinear causal relationships between premise attributes and consequents.…”

Section: Introductionmentioning

confidence: 99%

Self-Organizing Fuzzy Belief Inference System for Classification

Angelov

Shen

2022

IEEE Trans. Fuzzy Syst.

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Evolving fuzzy systems (EFSs) are widely known as a powerful tool for streaming data prediction. In this paper, a novel zero-order EFS with a unique belief structure is proposed for data stream classification. Thanks to this new belief structure, the proposed model can handle the inter-class overlaps in a natural way and better capture the underlying multi-model structure of data streams in the form of prototypes. Utilizing data-driven soft thresholds, the proposed model self-organizes a set of prototype-based IF-THEN fuzzy belief rules from data streams for classification, and its learning outcomes are practically meaningful. With no requirement of prior knowledge in the problem domain, the proposed model is capable of selfdetermining the appropriate level of granularity for rule base construction, while enabling users to specify their preferences on the degree of fineness of its knowledge base. Numerical examples demonstrate the superior performance of the proposed model on a wide range of stationary and nonstationary classification benchmark problems.

show abstract

Ensemble Belief Rule-Based Model for complex system classification and prediction

Cited by 26 publications

References 33 publications

Multistep prediction of dynamic uncertainty under limited data

Multistep prediction of dynamic uncertainty under limited data

Optimal Extreme Random Forest Ensemble for Active Distribution Network Forecasting-Aided State Estimation Based on Maximum Average Energy Concentration VMD State Decomposition

Self-Organizing Fuzzy Belief Inference System for Classification

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