His main research concerns Artificial intelligence, Mathematical optimization, Artificial neural network, Control theory and Pattern recognition. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning and Computer vision. His research in Mathematical optimization intersects with topics in Matching, Graduated optimization and Partial permutation.
Hong Qiao works mostly in the field of Artificial neural network, limiting it down to concerns involving Exponential stability and, occasionally, Recurrent neural network, Applied mathematics and Lipschitz continuity. His biological study spans a wide range of topics, including Stochastic process, Bilinear interpolation and Matrix. His Discriminative model study in the realm of Pattern recognition interacts with subjects such as Competitive algorithm.
Artificial intelligence, Computer vision, Pattern recognition, Algorithm and Machine learning are his primary areas of study. Feature extraction, Feature, Support vector machine, Object detection and Image are the core of his Artificial intelligence study. His Computer vision research incorporates elements of Robustness and GRASP.
He combines subjects such as Contextual image classification and Cluster analysis with his study of Pattern recognition. His research in Machine learning is mostly concerned with Artificial neural network. His Matching research is multidisciplinary, incorporating elements of Combinatorics and Relaxation.
His primary areas of study are Artificial intelligence, Robot, Computer vision, Pattern recognition and Artificial neural network. Many of his studies on Artificial intelligence apply to Machine learning as well. Hong Qiao has included themes like Robotic arm and Robustness in his Robot study.
His Computer vision study integrates concerns from other disciplines, such as Selection method, Moment and Benchmark. The concepts of his Pattern recognition study are interwoven with issues in Subspace topology and Data set. His Artificial neural network study combines topics from a wide range of disciplines, such as Encoding, State space, Forgetting, Image and Salient object detection.
His primary scientific interests are in Artificial intelligence, Pattern recognition, Reinforcement learning, Algorithm and Artificial neural network. His study ties his expertise on Computer vision together with the subject of Artificial intelligence. His Pattern recognition research is multidisciplinary, relying on both Focus, Metric and Laplace operator.
Hong Qiao has researched Reinforcement learning in several fields, including Control and Human–computer interaction. His Algorithm research incorporates themes from Rough set, Task and Outlier. His studies deal with areas such as Robot and Robustness as well as Artificial neural network.
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On stabilization of bilinear uncertain time-delay stochastic systems with Markovian jumping parameters
Zidong Wang;Hong Qiao;K.J. Burnham.
IEEE Transactions on Automatic Control (2002)
A reference model approach to stability analysis of neural networks
Hong Qiao;Jigen Peng;Z.-B. Xu;Bo Zhang.
systems man and cybernetics (2003)
Nonlinear measures: a new approach to exponential stability analysis for Hopfield-type neural networks
Hong Qiao;Jigen Peng;Zong-Ben Xu.
IEEE Transactions on Neural Networks (2001)
Disruption of xCT inhibits cancer cell metastasis via the caveolin-1/β-catenin pathway
Chen Rs;Song Ym;Zhou Zy;Tong T.
A comparative study of two modeling approaches in neural networks
Zong-Ben Xu;Hong Qiao;Jigen Peng;Bo Zhang.
Neural Networks (2004)
Event-Triggered State Estimation for Discrete-Time Multidelayed Neural Networks With Stochastic Parameters and Incomplete Measurements
Bo Shen;Zidong Wang;Hong Qiao.
IEEE Transactions on Neural Networks (2017)
A simple Taylor-series expansion method for a class of second kind integral equations
Yuhe Ren;Bo Zhang;Hong Qiao.
Journal of Computational and Applied Mathematics (1999)
Discriminatively Boosted Image Clustering with Fully Convolutional Auto-Encoders
Fengfu Li;Hong Qiao;Hong Qiao;Bo Zhang.
Pattern Recognition (2018)
A new approach to stability of neural networks with time-varying delays
Jigen Peng;Hong Qiao;Zong-ben Xu.
Neural Networks (2002)
A Scale Stretch Method Based on ICP for 3D Data Registration
Shihui Ying;Jigen Peng;Shaoyi Du;Hong Qiao.
IEEE Transactions on Automation Science and Engineering (2009)
Profile was last updated on December 6th, 2021.
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