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Wu Ai

Professor, School of Mathematics and Statistics, Guilin University of Technology

Publications

Journal articles

  1. Chen H, Tang G, Song Q and Ai W (2013), “Combination of Modified Optical Path Length Estimation and Correction and Moving Window Partial Least Squares to Waveband Selection for the Fourier Transform Near-Infrared Determination of Pectin in Shaddock Peel”, ANALYTICAL LETTERS., September, 2013. Vol. 46(13), pp. 2060-2074.
    [BibTeX] [DOI]
  2. Chen H, Ai W, Feng Q, Jia Z and Song Q (2014), “FT-NIR spectroscopy and Whittaker smoother applied to joint analysis of duel-components for corn”, SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY., January, 2014. Vol. 118, pp. 752-759.
    [BibTeX] [DOI]
  3. Gu A and Ai W (2014), “Random attractor for stochastic lattice dynamical systems with alpha-stable Levy noises”, COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION., May, 2014. Vol. 19(5), pp. 1433-1441.
    [BibTeX] [DOI]
  4. Chen H-Z, Ai W, Feng Q-X and Tang G-Q (2015), “FT-MIR modelling enhancement for the quantitative determination of haemoglobin in human blood by combined optimization of grid-search LSSVR algorithm with different pre-processing modes”, ANALYTICAL METHODS. Vol. 7(6), pp. 2869-2876.
    [BibTeX] [DOI]
  5. Chen H-Z, Tang G-Q, Ai W, Xu L-L and Cai K (2015), “Use of Random Forest in FTIR Analysis of LDL Cholesterol and Tri-Glycerides for Hyperlipidemia”, BIOTECHNOLOGY PROGRESS., NOV-DEC, 2015. Vol. 31(6), pp. 1693-1702.
    [BibTeX] [DOI]
  6. Ai W, Chen W and Xie J (2016), “A zero-gradient-sum algorithm for distributed cooperative learning using a feedforward neural network with random weights”, INFORMATION SCIENCES., December, 2016. Vol. 373, pp. 404-418.
    [BibTeX] [DOI]
  7. Ai W, Chen W and Xie J (2017), “Distributed learning for feedforward neural networks with random weights using an event-triggered communication scheme”, NEUROCOMPUTING., February, 2017. Vol. 224, pp. 184-194.
    [BibTeX] [DOI]
  8. Ai W, Chen W and Xie J (2017), “A general framework for population-based distributed optimization over networks”, INFORMATION SCIENCES., December, 2017. Vol. 418, pp. 136-152.
    [BibTeX] [DOI]
  9. Ai W, Chen W and Hua S (2018), “Distributed cooperative learning for a group of uncertain systems via output feedback and neural networks”, JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS., March, 2018. Vol. 355(5), pp. 2536-2561.
    [BibTeX] [DOI]
  10. Chen H, Liu Z, Gu J, Ai W, Wen J and Cai K (2018), “Quantitative analysis of soil nutrition based on FT-NIR spectroscopy integrated with BP neural deep learning”, ANALYTICAL METHODS., November, 2018. Vol. 10(41), pp. 5004-5013.
    [BibTeX] [DOI]
  11. Xie J, Chen W, Dai H, Liu S and Ai W (2019), “A distributed cooperative learning algorithm based on Zero-Gradient-Sum strategy using Radial Basis Function Network”, NEUROCOMPUTING., January, 2019. Vol. 323, pp. 244-255.
    [BibTeX] [DOI]
  12. Zhang X, Wu J, Ai W and Li J (2019), “Adaptive Neural Control with Prespecified Tracking Accuracy for a Class of Switched Systems Subject to Input Delay”, COMPLEXITY., July, 2019. Vol. 2019
    [BibTeX] [DOI]
  13. Chen H, Xu L, Ai W, Lin B, Feng Q and Cai K (2020), “Kernel functions embedded in support vector machine learning models for rapid water pollution assessment via near-infrared spectroscopy”, SCIENCE OF THE TOTAL ENVIRONMENT., April, 2020. Vol. 714
    [BibTeX] [DOI]
  14. Ai W and Wang D (2020), “Distributed stochastic configuration networks with cooperative learning paradigm”, INFORMATION SCIENCES., November, 2020. Vol. 540, pp. 1-16.
    [BibTeX] [DOI]
  15. Li S, Ai W, Wu J and Feng Q (2021), “A Fixed-time Distributed Algorithm for Least Square Solutions of Linear Equations”, INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS., March, 2021. Vol. 19(3), pp. 1311-1318.
    [BibTeX] [DOI]
  16. Cai K, Chen H, Ai W, Miao X, Lin Q and Feng Q (2022), “Feedback Convolutional Network for Intelligent Data Fusion Based on Near-Infrared Collaborative IoT Technology”, IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS., February, 2022. Vol. 18(2), pp. 1200-1209.
    [BibTeX] [DOI]
  17. Li C, Chen H, Zhang Y, Hong S, Ai W and Mo L (2022), “Improvement of NIR prediction ability by dual model optimization in fusion of NSIA and SA methods”, SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY., August, 2022. Vol. 276
    [BibTeX] [DOI]
  18. Zhou Y, Ge X and Ai W (2022), “ Communication-Censored Distributed Learning for Stochastic Configuration Networks”, INTERNATIONAL JOURNAL OF INTELLIGENCE SCIENCE., April, 2022. Vol. 12(2), pp. 21-37.
    [BibTeX] [DOI]
  19. Qiu W, Ai W, Chen H, Feng Q and Tang G (2023), “Decentralized Federated Learning for Industrial IoT with Deep Echo State Networks”, IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS. April, 2023, Vol. 19(4), pp. 5849-5857.
    [BibTeX] [DOI]
  20. Chen H, Li X, Meng F, Ai W, Lin Q and Cai K (2023), “A Decision Tree Network with Semi-Supervised Entropy Learning Strategy for Spectroscopy Aided Detection of Blood Hemoglobin”, SPECTROCHIMICA ACTA PART A: MOLECULAR AND BIOMOLECULAR SPECTROSCOPY. April 2023. Vol. 291, pp. 122354.
    [BibTeX] [DOI]
  21. Liang J, Ai W, Chen H and Tang G (2023), “Communication-efficient decentralized elastic-net broad learning system based on quantized and censored communications”, APPLIED SOFT COMPUTING. February, 2023. Vol. 134, pp. 109999.
    [BibTeX] [DOI]
  22. Zhou Y, Ai W, Tang G, Chen H (2023), “Distributed sparse learning for stochastic configuration networks via alternating direction method of multipliers”, APPLIED INTELLIGENCE., 2023, Vol. 53, pp. 23522–23537.
    [BibTeX] [DOI]
  23. Jin P, Cen J, Feng Q, Ai W, Chen H, Qiao H (2024), “Differential evolution with the mutation strategy transformation based on a quartile for numerical optimization”, APPLIED INTELLIGENCE., 2024, Vol. 54, pp. 334–356.
    [BibTeX] [DOI]

Conference papers

  1. Yang X, Li J, Ai W, Wu J (2017) Adaptive Control Design for a Class of Switched Uncertain Nonlinear Systems with Known Tracking Accuracy. In: 2017 CHINESE AUTOMATION CONGRESS (CAC), Chinese Automation Congress, pp 5119-5123
  2. Li S, Ai W, Wu J (2018) Self-Triggered Distributed Optimization for Multi-Agent Systems with Periodic Sampling Schemes. In: 2018 CHINESE AUTOMATION CONGRESS (CAC), Chinese Automation Congress, pp 561-566
  3. Ai W, Li S, Wu J, Chen H, Feng Q, Lin B (2019) Parallel Training for Large-Scale Echo State Networks via Alternating Direction Method of Multipliers. In: 2019 CHINESE AUTOMATION CONGRESS (CAC), Chinese Automation Congress, pp 350-355
  4. Ai W, Li S, Wu J, Feng Q (2019) A Fixed-Time Distributed Algorithm for Least Square Solutions of Linear Equations. In: PROCEEDINGS OF THE 2019 31ST CHINESE CONTROL AND DECISION CONFERENCE (CCDC), Chinese Control and Decision Conference, pp 886-890
  5. Li S, Ai W, Ge X (2020) Fixed-Time Distributed Cooperative Learning for Stochastic Configuration Networks. In: 2020 CHINESE AUTOMATION CONGRESS (CAC), Chinese Automation Congress, pp 3380-3383
  6. Zhou Y, Ai W (2020) ADMM-Based Sparse Distributed Learning for Stochastic Configuration Networks. In: 2020 CHINESE AUTOMATION CONGRESS (CAC), Chinese Automation Congress, pp 4354-4358
  7. Ge X, Ai W, Zhou Y (2020) Distributed Event-Triggered Learning for Stochastic Configuration Networks. In: 2020 CHINESE AUTOMATION CONGRESS (CAC), Chinese Automation Congress, pp 3384-3388
  8. Liang J, Ai W (2021) Distributed Broad Learning System With Quantized and Censored Communications. In: 2021 CHINESE AUTOMATION CONGRESS (CAC), Chinese Automation Congress, pp 7516-7520
  9. Qiu W, Ai W (2021) A Decentralized Algorithm for Deep Echo State Networks. In: 2021 CHINESE AUTOMATION CONGRESS (CAC), Chinese Automation Congress, pp 841-845
  10. Zhou M, Ai W (2021) Distributed Reduced Kernel Extreme Learning Machine. In: 2021 CHINESE AUTOMATION CONGRESS (CAC), Chinese Automation Congress, pp 3384-3387

Thesis

  • Wu Ai, “Distributed Cooperative Learning over Peer-to-Peer Networks,” Doctoral Dissertation, School of Mathematics and Statistics, Xidian University, Xi’an, China, June 2017