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 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.
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.
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.
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Recursive Estimation and Time Series Analysis
Peter C. Young.
Journal of the Royal Statistical Society: Series A (General) (1984)
Parameter estimation for continuous-time models-A survey
Direct Identification of Continuous-time Models from Sampled Data: Issues, Basic Solutions and Relevance
Hugues Garnier;Liuping Wang;Peter C. Young;Peter C. Young.
An instrumental variable method for real-time identification of a noisy process
Dynamic harmonic regression.
Peter C. Young;Diego J. Pedregal;Wlodek Tych.
Journal of Forecasting (1999)
Recursive Estimation and Time-Series Analysis: An Introduction
Data-based mechanistic modelling of environmental, ecological, economic and engineering systems.
Peter C. Young.
Environmental Modelling and Software (1998)
Uncertainty, Complexity and Concepts of Good Science in Climate Change Modelling: Are GCMs the Best Tools?
Simon Shackley;Peter Young;Stuart Parkinson;Brian Wynne.
Data-based mechanistic modelling and the rainfall-flow non-linearity.
Peter C. Young;Keith J. Beven.
Refined instrumental variable methods of recursive time-series analysis Part III. Extensions
Peter Young;Anthony Jakeman.
International Journal of Control (1980)
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