What is he best known for?
The fields of study he is best known for:
- Statistics
- Algorithm
- Control theory
Feng Ding mainly investigates Estimation theory, Algorithm, Mathematical optimization, Least squares and Control theory.
His Estimation theory research incorporates themes from Moving average, Scalar, Applied mathematics, Nonlinear system and System identification.
In most of his Algorithm studies, his work intersects topics such as Autoregressive–moving-average model.
His Mathematical optimization research focuses on subjects like Signal processing, which are linked to State.
His Least squares research includes themes of Iterative method, Key, Recursive least squares filter and Identification.
Feng Ding usually deals with Control theory and limits it to topics linked to Dual and Stochastic process.
His most cited work include:
- Gradient based iterative algorithms for solving a class of matrix equations (362 citations)
- Identification of Hammerstein nonlinear ARMAX systems (339 citations)
- Iterative least-squares solutions of coupled Sylvester matrix equations (327 citations)
What are the main themes of his work throughout his whole career to date?
Estimation theory, Algorithm, Identification, Least squares and Control theory are his primary areas of study.
The Estimation theory study combines topics in areas such as Stochastic process, Mathematical optimization, Applied mathematics, System identification and Nonlinear system.
He has researched Algorithm in several fields, including Autoregressive model and Autoregressive–moving-average model.
His biological study deals with issues like Multivariate statistics, which deal with fields such as Equation error.
In his study, State observer is strongly linked to Bilinear interpolation, which falls under the umbrella field of Least squares.
His State space study incorporates themes from Kalman filter, State-space representation and State.
He most often published in these fields:
- Estimation theory (88.66%)
- Algorithm (82.90%)
- Identification (56.89%)
What were the highlights of his more recent work (between 2017-2021)?
- Estimation theory (88.66%)
- Algorithm (82.90%)
- Identification (56.89%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Estimation theory, Algorithm, Identification, Autoregressive model and Nonlinear system.
His Estimation theory research is multidisciplinary, relying on both Control theory, Multivariate statistics, Moving average and Applied mathematics.
His studies deal with areas such as Multivariable calculus and Autoregressive–moving-average model as well as Algorithm.
His research in Identification tackles topics such as Maximum likelihood which are related to areas like Coupling.
In his study, which falls under the umbrella issue of Autoregressive model, Artificial intelligence is strongly linked to Identification methods.
The Recursive least squares filter study which covers System identification that intersects with Matrix.
Between 2017 and 2021, his most popular works were:
- State estimation for bilinear systems through minimizing the covariance matrix of the state estimation errors (92 citations)
- State estimation for bilinear systems through minimizing the covariance matrix of the state estimation errors (92 citations)
- State estimation for bilinear systems through minimizing the covariance matrix of the state estimation errors (92 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Algorithm
- Control theory
Feng Ding mostly deals with Algorithm, Estimation theory, Identification, Iterative method and Least squares.
His work deals with themes such as Autoregressive model, Autoregressive–moving-average model, Parameter identification problem, Multivariate statistics and Nonlinear system, which intersect with Algorithm.
His research integrates issues of Key and Multivariable calculus, Control theory in his study of Autoregressive–moving-average model.
Feng Ding combines subjects such as Stochastic process, Applied mathematics, State observer and System identification with his study of Estimation theory.
His Iterative method study integrates concerns from other disciplines, such as Dynamical systems theory, Representation and Type.
His Least squares research is multidisciplinary, incorporating elements of State-space representation, Linear system and Recursive least squares filter.
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