Ying Tan mainly investigates Mathematical optimization, Artificial intelligence, Benchmark, Control theory and Swarm intelligence. His work deals with themes such as Stability, Convergence and Arbitrarily large, which intersect with Mathematical optimization. Ying Tan has researched Artificial intelligence in several fields, including Algorithm, Machine learning and Pattern recognition.
His studies in Benchmark integrate themes in fields like Evolutionary computation, Global optimum, Speedup and Surrogate model. His study connects Wavelet and Control theory. His biological study spans a wide range of topics, including Swarm robotics, Swarm behaviour and Optimization problem.
Ying Tan focuses on Artificial intelligence, Control theory, Mathematical optimization, Algorithm and Nonlinear system. His Artificial intelligence study integrates concerns from other disciplines, such as Swarm intelligence, Machine learning and Pattern recognition. His is doing research in Iterative learning control, Control theory, Trajectory, Stability and Robustness, both of which are found in Control theory.
The study incorporates disciplines such as Function, Iterative method and Adaptive control in addition to Iterative learning control. His studies deal with areas such as Convergence and Benchmark as well as Mathematical optimization. A large part of his Nonlinear system studies is devoted to Exponential stability.
Artificial intelligence, Control theory, Benchmark, Machine learning and Mathematical optimization are his primary areas of study. Nonlinear system, Iterative learning control, Robustness, Control theory and Trajectory are the primary areas of interest in his Control theory study. His study in Iterative learning control is interdisciplinary in nature, drawing from both Function and Convergence.
His Robustness study incorporates themes from Control system, Bounded function and Inverted pendulum. His Benchmark research focuses on subjects like Algorithm, which are linked to Fitness landscape. His study in Evolutionary algorithm, Optimization problem, Particle swarm optimization and Surrogate model is carried out as part of his Mathematical optimization studies.
His primary areas of study are Benchmark, Artificial intelligence, Mathematical optimization, Control theory and Fireworks algorithm. His work carried out in the field of Benchmark brings together such families of science as Point, Algorithm and Swarm behaviour. His study focuses on the intersection of Algorithm and fields such as Fitness landscape with connections in the field of Convergence and Rework.
His Artificial intelligence study combines topics in areas such as Machine learning, Position and Pattern recognition. His work on Particle swarm optimization and Evolutionary algorithm as part of his general Mathematical optimization study is frequently connected to Test suite, thereby bridging the divide between different branches of science. His Control theory research is multidisciplinary, incorporating perspectives in Position sensor and Robot end effector.
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Fireworks algorithm for optimization
Ying Tan;Yuanchun Zhu.
international conference on swarm intelligence (2010)
Linear and Nonlinear Iterative Learning Control
Jian-Xin Xu;Ying Tan.
On non-local stability properties of extremum seeking control
Ying Tan;Dragan Nešić;Iven Mareels.
Energy Harvesting From Hybrid Indoor Ambient Light and Thermal Energy Sources for Enhanced Performance of Wireless Sensor Nodes
Yen Kheng Tan;S. K. Panda.
IEEE Transactions on Industrial Electronics (2011)
Extremum seeking from 1922 to 2010
Y. Tan;W.H. Moase;C. Manzie;D. Nesic.
chinese control conference (2010)
Research Advance in Swarm Robotics
Ying Tan;Zhong-yang Zheng.
Defence Technology (2013)
Enhanced Fireworks Algorithm
Shaoqiu Zheng;Andreas Janecek;Ying Tan.
congress on evolutionary computation (2013)
Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN
Weiwei Hu;Ying Tan.
arXiv: Learning (2017)
GPU-based parallel particle swarm optimization
You Zhou;Ying Tan.
congress on evolutionary computation (2009)
Nonlinear blind source separation using a radial basis function network
Ying Tan;Jun Wang;J.M. Zurada.
IEEE Transactions on Neural Networks (2001)
Profile was last updated on December 6th, 2021.
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