What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Control theory
- Mathematical analysis
His scientific interests lie mostly in Control theory, Artificial neural network, Recurrent neural network, Quadratic programming and Mathematical optimization.
His Control theory research is multidisciplinary, incorporating elements of Robot, Motion control, Robotic arm, Redundancy and Solver.
His Artificial neural network research is multidisciplinary, incorporating perspectives in Discontinuity, Algorithm, Matrix and Robustness.
His Recurrent neural network study combines topics from a wide range of disciplines, such as Moore–Penrose pseudoinverse, Applied mathematics and Nonlinear system.
The Quadratic programming study combines topics in areas such as Variational inequality, Quadratic equation and Realizability.
His study explores the link between Mathematical optimization and topics such as Numerical analysis that cross with problems in Zero finding.
His most cited work include:
- Design and analysis of a general recurrent neural network model for time-varying matrix inversion (332 citations)
- A recurrent neural network for solving Sylvester equation with time-varying coefficients (310 citations)
- A unified quadratic-programming-based dynamical system approach to joint torque optimization of physically constrained redundant manipulators (216 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Control theory, Artificial neural network, Applied mathematics, Quadratic programming and Mathematical optimization.
His work focuses on many connections between Control theory and other disciplines, such as Tracking, that overlap with his field of interest in Control.
In most of his Artificial neural network studies, his work intersects topics such as Algorithm.
His Applied mathematics research incorporates elements of Matrix, Discrete time and continuous time, Error function, Zhàng and Discretization.
Yunong Zhang combines subjects such as Robot, Motion planning, Quadratic equation, Variational inequality and Solver with his study of Quadratic programming.
His Recurrent neural network research is multidisciplinary, incorporating elements of Norm and Activation function.
He most often published in these fields:
- Control theory (47.18%)
- Artificial neural network (31.69%)
- Applied mathematics (31.69%)
What were the highlights of his more recent work (between 2018-2021)?
- Applied mathematics (31.69%)
- Discretization (16.73%)
- Control theory (47.18%)
In recent papers he was focusing on the following fields of study:
Yunong Zhang mainly investigates Applied mathematics, Discretization, Control theory, Artificial neural network and Nonlinear system.
His studies in Applied mathematics integrate themes in fields like Zhàng, Matrix and Type.
His studies deal with areas such as Algorithm, Truncation error, Stability, Instant and Discrete time and continuous time as well as Discretization.
He studies Control theory which is a part of Control theory.
His Artificial neural network study incorporates themes from Motion control, Structure, Linear equation and Error function.
His research investigates the connection between Nonlinear system and topics such as Scalar that intersect with issues in Mathematical analysis and Special case.
Between 2018 and 2021, his most popular works were:
- General four-step discrete-time zeroing and derivative dynamics applied to time-varying nonlinear optimization (18 citations)
- New Discrete-Solution Model for Solving Future Different-Level Linear Inequality and Equality With Robot Manipulator Control (16 citations)
- Two New Discrete-Time Neurodynamic Algorithms Applied to Online Future Matrix Inversion With Nonsingular or Sometimes-Singular Coefficient (15 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Mathematical analysis
- Algebra
Discretization, Applied mathematics, Artificial neural network, Discrete time and continuous time and Time derivative are his primary areas of study.
He has included themes like Stability, Quadratic programming, Algorithm, Residual and Numerical analysis in his Discretization study.
His Applied mathematics research includes themes of Numerical differentiation, Basis, Euler's formula, Function and Transpose.
His Artificial neural network study integrates concerns from other disciplines, such as Motion control, Control, Linear inequality and Error function.
Yunong Zhang focuses mostly in the field of Time derivative, narrowing it down to matters related to Nonlinear system and, in some cases, Control theory.
Robot manipulator is a subfield of Control theory that Yunong Zhang investigates.
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