Model Identification and Data Analysis

This book provides a comprehensive approach to constructing models from experimental data, covering topics such as statistical data prediction, Kalman filtering, black-box model identification, parameter estimation, spectral analysis, and predictive control. Written for graduate students, it offers accessible methods explained step-by-step in simple terms and includes an overview on random variables and stationary processes, as well as an introduction to discrete time models and matrix analysis.

Format: Hardback
Length: 416 pages
Publication date: 14 June 2019
Publisher: John Wiley and Sons Ltd

This comprehensive textbook delves into the intricate process of constructing models from experimental data, encompassing a wide array of topics and applications. Written with graduate students in mind, it provides a systematic and effective approach that has proven successful in the author's years of teaching these subjects at his University.

The book offers accessible methods that are explained step-by-step in simple terms, making it accessible to a broad audience, including students and professionals from various fields such as engineering, statistics, and mathematics. It serves as an essential tool that is applicable in diverse areas, particularly in engineering where it is used to simulate and predict complex systems.

In addition to its theoretical foundations, the book provides an overview on random variables and stationary processes, as well as an introduction to discrete time models and matrix analysis. This comprehensive coverage enables readers to gain a deep understanding of the underlying principles and techniques employed in model construction.

What sets this textbook apart is its inclusion of historical commentaries that place the developments in the discipline into perspective. These commentaries provide valuable insights into the historical roots and evolution of the field, allowing readers to appreciate the rich heritage and advancements that have shaped the current state of the art.

To complement the theoretical presentation, the book includes numerous examples and solved problems. These examples help to illustrate the practical applications of the techniques discussed and facilitate a better understanding of the concepts presented. By working through these problems, readers can develop their problem-solving skills and apply the knowledge gained in the textbook to real-world scenarios.

In summary, this textbook is a valuable resource for graduate students and professionals seeking to develop their skills in constructing models from experimental data. With its comprehensive coverage, accessible language, and practical examples, it provides a solid foundation for understanding and applying these techniques in a wide range of applications. Whether you are a researcher, engineer, or analyst, this textbook will be an invaluable tool in your pursuit of knowledge and excellence.

Weight: 774g
Dimension: 160 x 237 x 21 (mm)
ISBN-13: 9781119546368