linear data-smoothing and prediction in theory and practice

Non-Linear Predictive Control: Theory an

📁 Non-Linear Predictive Control: Theory and Practice

✍ Basil Kouvaritakis (editor), Mark Cannon (editor) 📂 Library 📅 2001 🏛 IET 🌐 English

Model based predictive control has proved to be a fertile area of research, but above all has gained enormous success with industry, especially in the context of process control. Non-linear model based predictive control is of particular interest as this best represents the dynamics of most real pla

Kernel Smoothing in MATLAB: Theory and P

📁 Kernel Smoothing in MATLAB: Theory and Practice of Kernel Smoothing

✍ Ivanka Horova, Jan Kolacek, Jiri Zelinka 📂 Library 📅 2012 🏛 World Scientific 🌐 English

Methods of kernel estimates represent one of the most effective nonparametric smoothing techniques. These methods are simple to understand and they possess very good statistical properties. This book provides a concise and comprehensive overview of statistical theory and in addition, emphasis is giv

Non-Linear Predictive Control: Theory &

📁 Non-Linear Predictive Control: Theory & Practice

✍ Basil Kouvaritakis, Mark Cannon 📂 Library 📅 2001 🏛 The Institution of Engineering and Technology 🌐 English

Model based predictive control has proved to be a fertile area of research, but above all has gained enormous success with industry, especially in the context of process control. Non-linear model based predictive control is of particular interest as this best represents the dynamics of most real pla

Linear and Integer Programming: Theory a

📁 Linear and Integer Programming: Theory and Practice

✍ Sierksma G. 📂 Library 📅 2002 🏛 Dekker 🌐 English

"Combines the theoretical and practical aspects of linear and integer programming. Provides practical case studies and techniques, including rounding-off, column-generation, game theory, multiobjective optimization, and goal programming, as well as real-world solutions to the transportation and tran

Linear and Integer Programming: Theory a

📁 Linear and Integer Programming: Theory and Practice

✍ Gerard Sierksma 📂 Library 📅 2001 🏛 CRC Press 🌐 English

"Combines the theoretical and practical aspects of linear and integer programming. Provides practical case studies and techniques, including rounding-off, column-generation, game theory, multiobjective optimization, and goal programming, as well as real-world solutions to the transportation and tran

Prediction, filtering and smoothing in n

📁 Prediction, filtering and smoothing in non-linear and non-normal cases using Monte Carlo integration

✍ Tanizaki H., Mariano R.S. 📂 Library 📅 1994 🌐 English

A simulation-based non-linear filter is developed for prediction and smoothing in non-linear and/or non-normal structural time-series models. Recursive algorithms of weighting functions are derived by applying Monte Carlo integration. Through Monte Carlo experiments, it is shown that (1) for a small