Data Driven Model Learning for Engineers: With Applications to Univariate Time Series

Mercère, Guillaume

  • 出版商: Springer
  • 出版日期: 2024-08-11
  • 售價: $3,650
  • 貴賓價: 9.5$3,468
  • 語言: 英文
  • 頁數: 212
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 303131638X
  • ISBN-13: 9783031316388
  • 海外代購書籍(需單獨結帳)

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商品描述

The main goal of this comprehensive textbook is to cover the core techniques required to understand some of the basic and most popular model learning algorithms available for engineers, then illustrate their applicability directly with stationary time series. A multi-step approach is introduced for modeling time series which differs from the mainstream in the literature. Singular spectrum analysis of univariate time series, trend and seasonality modeling with least squares and residual analysis, and modeling with ARMA models are discussed in more detail.
As applications of data-driven model learning become widespread in society, engineers need to understand its underlying principles, then the skills to develop and use the resulting data-driven model learning solutions. After reading this book, the users will have acquired the background, the knowledge and confidence to (i) read other model learning textbooks more easily, (ii) use linear algebra and statistics for data analysis and modeling, (iii) explore other fields of applications where model learning from data plays a central role. Thanks to numerous illustrations and simulations, this textbook will appeal to undergraduate and graduate students who need a first course in data-driven model learning. It will also be useful for practitioners, thanks to the introduction of easy-to-implement recipes dedicated to stationary time series model learning. Only a basic familiarity with advanced calculus, linear algebra and statistics is assumed, making the material accessible to students at the advanced undergraduate level.

商品描述(中文翻譯)

這本綜合性教科書的主要目標是涵蓋工程師理解一些基本且最受歡迎的模型學習演算法所需的核心技術,然後直接用穩態時間序列來說明它們的適用性。書中介紹了一種多步驟的方法來建模時間序列,這種方法與文獻中的主流方法有所不同。對於單變量時間序列的奇異譜分析、使用最小二乘法和殘差分析進行的趨勢和季節性建模,以及使用ARMA模型進行建模的內容將進行更詳細的討論。

隨著數據驅動模型學習的應用在社會中變得普遍,工程師需要理解其基本原則,然後掌握開發和使用所產生的數據驅動模型學習解決方案的技能。閱讀完這本書後,讀者將獲得背景知識和信心,以便 (i) 更輕鬆地閱讀其他模型學習教科書,(ii) 使用線性代數和統計學進行數據分析和建模,(iii) 探索其他應用領域,其中數據驅動的模型學習扮演著核心角色。得益於大量的插圖和模擬,這本教科書將吸引需要數據驅動模型學習入門課程的本科生和研究生。由於引入了易於實施的針對穩態時間序列模型學習的食譜,它對於實務工作者也將非常有用。書中假設讀者對高級微積分、線性代數和統計學有基本的熟悉程度,使得這些材料對於高年級本科生來說是可接觸的。

作者簡介

Guillaume Mercère received the M.S. degree in electrical engineering in 2001, the Ph.D. degree in automatic control (Lille University) in 2004 and the "Habilitation à diriger des Recherches" in 2012. Since September 2005, he has been an Associate Professor at Poitiers University, Poitiers, France, and a member of the Automatic Control and Electrical Engineering Laboratory of Poitiers. He was chair of the Electrical Energy Optimization and Control Department, Poitiers National School of Engineering, between 2010 and 2015. He was the co-leader of the French Technical Committee on System Identification between 2008 and 2014, then the chair of the IEEE CSS Technical Committee on System Identification and Adaptive Control between 2016 and 2019. He is currently an Associate Editor on the IEEE CSS Conference Editorial Board. He is the co-author of more than 80 international conference and journal papers. He has held visiting appointments at the University of Iceland, Nova SoutheasternUniversity in Florida (USA) and Politecnico di Milano in Italy. His main research interests include model learning and system identification theory, estimation theory, optimization theory, subspace-based identification for 1D and nD models, gray box and linear parameter varying system identification with a specific attention to state space models. His current activities focus on heat transfer, flexible and cable driven manipulators, aeronautics, vehicle tire/road interactions and image processing.

作者簡介(中文翻譯)

Guillaume Mercère於2001年獲得電機工程碩士學位,2004年在里爾大學獲得自動控制博士學位,並於2012年獲得「Habilitation à diriger des Recherches」。自2005年9月以來,他一直擔任法國普瓦捷大學的副教授,並且是普瓦捷自動控制與電機工程實驗室的成員。他在2010年至2015年間擔任普瓦捷國立工程學院電能優化與控制系的系主任。他曾於2008年至2014年間擔任法國系統識別技術委員會的共同領導,並於2016年至2019年間擔任IEEE CSS系統識別與自適應控制技術委員會的主席。目前,他是IEEE CSS會議編輯委員會的副編輯。他是80多篇國際會議和期刊論文的共同作者。他曾在冰島大學、美國佛羅里達州的Nova Southeastern University以及意大利的米蘭理工大學擔任訪問職位。他的主要研究興趣包括模型學習與系統識別理論、估計理論、優化理論、基於子空間的1D和nD模型識別、灰箱和線性參數變化系統識別,特別關注狀態空間模型。他目前的研究活動集中在熱傳遞、靈活和纜索驅動的機器人、航空學、車輛輪胎/路面互動以及影像處理。