Yingbiao Ling
According to our database1,
Yingbiao Ling
authored at least 14 papers
between 2013 and 2020.
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Bibliography
2020
Applying graph-based differential grouping for multiobjective large-scale optimization.
Swarm Evol. Comput., 2020
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2020
Peng-Type ZNN Model Attempted for Online Diagonalization of Time-Varying Symmetric Matrix.
Proceedings of the 12th International Conference on Advanced Computational Intelligence, 2020
Proceedings of the Computer Vision - ECCV 2020, 2020
2018
From Historical Data through ASF Method of Equal-Type Half Traversal to Predict USA Presidential Parties from 2021 to 2041.
Proceedings of the 2018 IEEE SmartWorld, 2018
Rough Period Estimation And Peak Prediction Of Stock Market Based On Multiple Sine Functions Extraction.
Proceedings of the 2018 International Conference on Machine Learning and Cybernetics, 2018
2017
Proceedings of the Neural Information Processing - 24th International Conference, 2017
Numerical prediction of short-term snowy weather in Guangzhou via addition-subtraction frequency (ASF) algorithm with unequally half traversal.
Proceedings of the 13th International Conference on Natural Computation, 2017
Possible M7.0-or-above chile earthquake numerically projected via full-traversal addition-subtraction frequency method.
Proceedings of the 13th International Conference on Natural Computation, 2017
2016
Proceedings of the IEEE International Conference on Web Services, 2016
Cooperative co-evolution with graph-based differential grouping for large scale global optimization.
Proceedings of the 12th International Conference on Natural Computation, 2016
2014
Three new ZNN models with economical dimension and exponential convergence for real-time solution of moore-penrose pseudoinverse.
Proceedings of the 2014 International Joint Conference on Neural Networks, 2014
2013
ZD and ZG controllers for explicit and implicit tracking of pendulum with singularity finally conquered.
Proceedings of the International Conference on Machine Learning and Cybernetics, 2013