What is he best known for?
The fields of study he is best known for:
- Statistics
- Control theory
- Artificial intelligence
Peter C. Young focuses on Mathematical optimization, Time series, Identification, Transfer function and Algorithm.
His Mathematical optimization research incorporates elements of Smoothing, Estimation theory, Stochastic modelling and Nonlinear system.
His research integrates issues of Instrumental variable, Autoregressive model, Estimation, Monte Carlo method and Operations research in his study of Time series.
His Identification study combines topics in areas such as Mathematical model, Industrial engineering, Mode and Artificial intelligence.
His Transfer function study combines topics from a wide range of disciplines, such as Kalman filter, Data assimilation and Flood forecasting.
His work in the fields of Algorithm, such as Model order, intersects with other areas such as Noise.
His most cited work include:
- Recursive Estimation and Time Series Analysis (581 citations)
- Parameter estimation for continuous-time models-A survey (478 citations)
- An instrumental variable method for real-time identification of a noisy process (358 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Control theory, Identification, Transfer function, Control engineering and Mathematical optimization.
Algorithm is closely connected to Instrumental variable in his research, which is encompassed under the umbrella topic of Identification.
The Transfer function study combines topics in areas such as Estimation theory, Monte Carlo method and Time series.
Many of his studies involve connections with topics such as Econometrics and Estimation theory.
He interconnects Control, Metering mode and Digital control in the investigation of issues within Control engineering.
He combines subjects such as Smoothing, Kalman filter and Stochastic modelling with his study of Mathematical optimization.
He most often published in these fields:
- Control theory (29.95%)
- Identification (28.02%)
- Transfer function (24.15%)
What were the highlights of his more recent work (between 2009-2021)?
- Identification (28.02%)
- Control theory (29.95%)
- Transfer function (24.15%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Identification, Control theory, Transfer function, System identification and Artificial intelligence.
His work carried out in the field of Identification brings together such families of science as MATLAB, Instrumental variable, Industrial engineering and Process.
His Instrumental variable research focuses on Mathematical optimization and how it relates to Applied mathematics.
His study in the fields of Nonlinear system, Control theory, Discrete time and continuous time and Realization under the domain of Control theory overlaps with other disciplines such as Noise.
The various areas that he examines in his Transfer function study include Estimation theory, Algorithm, Range, Simulation and Monte Carlo method.
His Time series research includes themes of Estimation and Econometrics.
Between 2009 and 2021, his most popular works were:
- The Wide Field Spectrograph (WiFeS): performance and data reduction (193 citations)
- The Wide Field Spectrograph (WiFeS): Performance and Data Reduction (192 citations)
- Recursive Estimation and Time-Series Analysis: An Introduction for the Student and Practitioner (151 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Control theory
- Artificial intelligence
The scientist’s investigation covers issues in Identification, System identification, Control theory, Transfer function and Time series.
His study in Identification is interdisciplinary in nature, drawing from both Variety, Artificial intelligence, MATLAB and Metamodeling.
His System identification research incorporates themes from Control system, State variable, Instrumental variable and Statistical model.
The concepts of his Control theory study are interwoven with issues in Recursive Bayesian estimation, Estimation theory and Robotics.
His studies in Transfer function integrate themes in fields like Kalman filter, Algorithm, Data assimilation and Applied mathematics.
His Time series research focuses on subjects like Simulation, which are linked to Biological system, Identifiability and Routing.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
