Solving Optimization Problems with the Heuristic Kalman Algorithm: New Stochastic Methods (使用啟發式卡爾曼演算法解決優化問題:新隨機方法)
Toscano, Rosario
- 出版商: Springer
- 出版日期: 2024-03-22
- 售價: $6,310
- 貴賓價: 9.5 折 $5,995
- 語言: 英文
- 頁數: 286
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 3031524586
- ISBN-13: 9783031524585
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相關分類:
Algorithms-data-structures
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相關主題
商品描述
This text focuses on simple and easy-to-use design strategies for solving complex engineering problems that arise in several fields of engineering design, namely non-convex optimization problems.
The main optimization tool used in this book to tackle the problem of nonconvexity is the Heuristic Kalman Algorithm (HKA). The main characteristic of HKA is the use of a stochastic search mechanism to solve a given optimization problem. From a computational point of view, the use of a stochastic search procedure appears essential for dealing with non-convex problems.
The topics discussed in this monograph include basic definitions and concepts from the classical optimization theory, the notion of the acceptable solution, machine learning, the concept of preventive maintenance, and more.
The Heuristic Kalman Algorithm discussed in this book applies to many fields such as robust structured control, electrical engineering, mechanical engineering, machine learning, reliability, and preference models. This large coverage of practical optimization problems makes this text very useful to those working on and researching systems design. The intended audience includes industrial engineers, postgraduates, and final-year undergraduates in various fields of systems design.
The main optimization tool used in this book to tackle the problem of nonconvexity is the Heuristic Kalman Algorithm (HKA). The main characteristic of HKA is the use of a stochastic search mechanism to solve a given optimization problem. From a computational point of view, the use of a stochastic search procedure appears essential for dealing with non-convex problems.
The topics discussed in this monograph include basic definitions and concepts from the classical optimization theory, the notion of the acceptable solution, machine learning, the concept of preventive maintenance, and more.
The Heuristic Kalman Algorithm discussed in this book applies to many fields such as robust structured control, electrical engineering, mechanical engineering, machine learning, reliability, and preference models. This large coverage of practical optimization problems makes this text very useful to those working on and researching systems design. The intended audience includes industrial engineers, postgraduates, and final-year undergraduates in various fields of systems design.
商品描述(中文翻譯)
本文本聚焦於簡單且易於使用的設計策略,以解決在多個工程設計領域中出現的複雜工程問題,特別是非凸優化問題。 本書中用來解決非凸性問題的主要優化工具是啟發式卡爾曼演算法(Heuristic Kalman Algorithm, HKA)。HKA的主要特點是使用隨機搜索機制來解決給定的優化問題。從計算的角度來看,使用隨機搜索程序對於處理非凸問題似乎是必不可少的。 本專著討論的主題包括經典優化理論中的基本定義和概念、可接受解的概念、機器學習、預防性維護的概念等。 本書中討論的啟發式卡爾曼演算法適用於許多領域,如穩健結構控制、電機工程、機械工程、機器學習、可靠性和偏好模型等。這種對實際優化問題的廣泛涵蓋使得本文本對於從事系統設計工作和研究的人士非常有用。預期的讀者包括工業工程師、研究生以及各個系統設計領域的最後一年本科生。
作者簡介
Rosario Toscano was born in Catania, Italy. He received his masters degree with specialization in control from the Institut National des Sciences Appliquées de Lyon in 1996. He received the Ph.D. degree from the Ecole Centrale de Lyon in 2000. He received the HDR degree (Habilitation to Direct Research) from the University Jean Monnet of Saint-Etienne in 2007. He is currently full professor at the Ecole Nationale d'Ingénieurs de Saint-Etienne and Ecole Centrale de Lyon (ENISE-ECL). His research interests include: structured controllers, robust control, stochastic optimization methods, dynamic reliability, fault detection, multimodel approach applied to diagnosis and control, fretting wear of mechanical surfaces and sensorial design of products.
作者簡介(中文翻譯)
Rosario Toscano 出生於義大利的卡塔尼亞。他於1996年在里昂國立應用科學學院獲得控制專業的碩士學位。2000年,他在里昂中央學院獲得博士學位。2007年,他在聖艾蒂安的讓·莫內大學獲得HDR學位(指導研究的資格)。他目前是聖艾蒂安國立工程學院和里昂中央學院(ENISE-ECL)的全職教授。他的研究興趣包括:結構控制器、穩健控制、隨機優化方法、動態可靠性、故障檢測、應用於診斷和控制的多模型方法、機械表面的摩擦磨損以及產品的感官設計。