Long Chen
Orcid: 0000-0003-4585-5662Affiliations:
- Dalian University of Technology, School of Control Science and Engineering, China
According to our database1,
Long Chen
authored at least 14 papers
between 2016 and 2024.
Collaborative distances:
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Bibliography
2024
Hybrid Physics- and Data-Driven Dynamic State Estimation of Electricity-Heat Integrated Energy Systems.
IEEE Trans. Instrum. Meas., 2024
2023
Time series prediction with input noise based on the ESN and the EM and its industrial applications.
Expert Syst. Appl., May, 2023
2022
Embedded Feature Selection Based on Relevance Vector Machines With an Approximated Marginal Likelihood and its Industrial Application.
IEEE Trans. Syst. Man Cybern. Syst., 2022
A probabilistic multi-criteria evaluation framework for integrated energy system planning.
Inf. Sci., 2022
2021
A Gaussian Approximation of Marginal Likelihood in Relevance Vector Machine for Industrial Data With Input Noise.
IEEE Trans. Instrum. Meas., 2021
Relevance Vector Machines-Based Time Series Prediction for Incomplete Training Dataset: Two Comparative Approaches.
IEEE Trans. Cybern., 2021
A review of system modeling, assessment and operational optimization for integrated energy systems.
Sci. China Inf. Sci., 2021
Artif. Intell. Rev., 2021
2020
A Novel Semi-Supervised Sparse Bayesian Regression Based on Variational Inference for Industrial Datasets With Incomplete Outputs.
IEEE Trans. Syst. Man Cybern. Syst., 2020
Variational inference based kernel dynamic Bayesian networks for construction of prediction intervals for industrial time series with incomplete input.
IEEE CAA J. Autom. Sinica, 2020
A deep granular network with adaptive unequal-length granulation strategy for long-term time series forecasting and its industrial applications.
Artif. Intell. Rev., 2020
Proceedings of the Advances in Neural Networks - ISNN 2020, 2020
2019
Variational Inference-Based Automatic Relevance Determination Kernel for Embedded Feature Selection of Noisy Industrial Data.
IEEE Trans. Ind. Electron., 2019
2016
Prediction intervals for industrial data with incomplete input using kernel-based dynamic Bayesian networks.
Artif. Intell. Rev., 2016