Nonlinear Filters: Theory and Applications (Hardcover)

NONLINEAR FILTERS

Discover the utility of using deep learning and (deep) reinforcement learning in deriving filtering algorithms with this insightful and powerful new resource

Nonlinear Filters: Theory and Applications delivers an insightful view on state and parameter estimation by merging ideas from control theory, statistical signal processing, and machine learning. Taking an algorithmic approach, the book covers both classic and machine learning-based filtering algorithms.

Readers of Nonlinear Filters will greatly benefit from the wide spectrum of presented topics including stability, robustness, computability, and algorithmic sufficiency. Readers will also enjoy:

• Organization that allows the book to act as a stand-alone, self-contained reference
• A thorough exploration of the notion of observability, nonlinear observers, and the theory of optimal nonlinear filtering that bridges the gap between different science and engineering disciplines
• A profound account of Bayesian filters including Kalman filter and its variants as well as particle filter
• A rigorous derivation of the smooth variable structure filter as a predictor-corrector estimator formulated based on a stability theorem, used to confine the estimated states within a neighborhood of their true values
• A concise tutorial on deep learning and reinforcement learning
• A detailed presentation of the expectation maximization algorithm and its machine learning-based variants, used for joint state and parameter estimation
• Guidelines for constructing nonparametric Bayesian models from parametric ones

Perfect for researchers, professors, and graduate students in engineering, computer science, applied mathematics, and artificial intelligence, Nonlinear Filters: Theory and Applications will also earn a place in the libraries of those studying or practicing in fields involving pandemic diseases, cybersecurity, information fusion, augmented reality, autonomous driving, urban traffic network, navigation and tracking, robotics, power systems, hybrid technologies, and finance.

「非線性濾波器」

以深度學習和(深度)強化學習為基礎，探索使用這種洞察力和強大的新資源來推導濾波算法的實用性。

「非線性濾波器：理論與應用」通過結合控制理論、統計信號處理和機器學習的思想，提供了對狀態和參數估計的深入理解。本書採用算法方法，涵蓋了經典的和基於機器學習的濾波算法。

閱讀「非線性濾波器」的讀者將從所呈現的廣泛主題中獲益，包括穩定性、魯棒性、可計算性和算法充分性。讀者還將享受到以下內容：

- 組織方式使本書成為一本獨立的、自成一體的參考資料
- 對可觀察性概念、非線性觀測器以及最優非線性濾波理論的深入探討，彌合了不同科學和工程學科之間的差距
- 對貝葉斯濾波器的深入介紹，包括卡爾曼濾波器及其變體以及粒子濾波器
- 對平滑變結構濾波器的嚴謹推導，作為基於穩定性定理的預測-校正估計器，用於將估計的狀態限制在其真實值的鄰域內
- 關於深度學習和強化學習的簡明教程
- 對期望最大化算法及其基於機器學習的變體的詳細介紹，用於聯合狀態和參數估計
- 構建非參數貝葉斯模型的指南，從參數模型中得出

非線性濾波器：理論與應用非常適合工程、計算機科學、應用數學和人工智能的研究人員、教授和研究生，也將成為在涉及流行病、網絡安全、信息融合、擴增實境、自動駕駛、城市交通網絡、導航和跟踪、機器人技術、電力系統、混合技術和金融等領域進行學習或實踐的人士的圖書館中的重要參考資料。

Peyman Setoodeh, PhD, is Visiting Professor with the Centre for Mechatronics and Hybrid Technologies (CMHT) at McMaster University. He is a Senior Member of the IEEE.

Saeid Habibi, PhD, is Professor and former Chair of the Department of Mechanical Engineering and the Director of the Centre for Mechatronics and Hybrid Technologies (CMHT) at McMaster University. He is a Fellow of the ASME and the CSME as well as a Canada Research Chair and a Senior NSERC Industrial Research Chair.

Simon Haykin, PhD, is Distinguished University Professor with the Department of Electrical and Computer Engineering and the Director of the Cognitive Systems Laboratory (CSL) at McMaster University. He is a Fellow of the IEEE and the Royal Society of Canada. He is a recipient of the Henry Booker Gold Medal from the International Union of Radio Science, the IEEE James H. Mulligan Jr. Education Medal, and the IEEE Denis J. Picard Medal for Radar Technologies and Applications.

Peyman Setoodeh, PhD, 是麥克馬斯特大學機電與混合技術中心（CMHT）的客座教授。他是IEEE的高級會員。

Saeid Habibi, PhD, 是麥克馬斯特大學機械工程系的教授和前系主任，也是機電與混合技術中心（CMHT）的主任。他是ASME和CSME的會士，同時也是加拿大研究主席和高級NSERC工業研究主席。

Simon Haykin, PhD, 是麥克馬斯特大學電機與計算機工程系的傑出大學教授，也是認知系統實驗室（CSL）的主任。他是IEEE和加拿大皇家學會的會士。他曾獲得國際無線電科學聯盟的亨利·布克爾金獎章，IEEE詹姆斯·H·穆利根教育獎章，以及IEEE丹尼斯·J·皮卡德雷達技術與應用獎章。