Modeling and Control of Uncertain Nonlinear Systems with Fuzzy Equations and Z-Number

An original, systematic-solution approach to uncertain nonlinear systems control and modeling using fuzzy equations and fuzzy differential equations

There are various numerical and analytical approaches to the modeling and control of uncertain nonlinear systems. Fuzzy logic theory is an increasingly popular method used to solve inconvenience problems in nonlinear modeling. Modeling and Control of Uncertain Nonlinear Systems with Fuzzy Equations and Z-Number presents a structured approach to the control and modeling of uncertain nonlinear systems in industry using fuzzy equations and fuzzy differential equations.

The first major work to explore methods based on neural networks and Bernstein neural networks, this innovative volume provides a framework for control and modeling of uncertain nonlinear systems with applications to industry. Readers learn how to use fuzzy techniques to solve scientific and engineering problems and understand intelligent control design and applications. The text assembles the results of four years of research on control of uncertain nonlinear systems with dual fuzzy equations, fuzzy modeling for uncertain nonlinear systems with fuzzy equations, the numerical solution of fuzzy equations with Z-numbers, and the numerical solution of fuzzy differential equations with Z-numbers. Using clear and accessible language to explain concepts and principles applicable to real-world scenarios, this book:

• Presents the modeling and control of uncertain nonlinear systems with fuzzy equations and fuzzy differential equations
• Includes an overview of uncertain nonlinear systems for non-specialists
• Teaches readers to use simulation, modeling and verification skills valuable for scientific research and engineering systems development
• Reinforces comprehension with illustrations, tables, examples, and simulations

Modeling and Control of Uncertain Nonlinear Systems with Fuzzy Equations and Z-Number is suitable as a textbook for advanced students, academic and industrial researchers, and practitioners in fields of systems engineering, learning control systems, neural networks, computational intelligence, and fuzzy logic control.

一本原創的系統解決方案方法，用於使用模糊方程和模糊微分方程對不確定的非線性系統進行控制和建模。

對於建模和控制不確定的非線性系統，有各種數值和分析方法。模糊邏輯理論是一種越來越受歡迎的方法，用於解決非線性建模中的不便問題。《使用模糊方程和Z數字對不確定的非線性系統進行建模和控制》提供了一種結構化方法，用於在工業中使用模糊方程和模糊微分方程對不確定的非線性系統進行控制和建模。

作為探索基於神經網絡和伯恩斯坦神經網絡方法的首個重要工作，這本創新的專著為控制和建模不確定的非線性系統提供了一個框架，並應用於工業領域。讀者將學習如何使用模糊技術解決科學和工程問題，並了解智能控制設計和應用。本書匯集了四年來關於使用雙模糊方程控制不確定的非線性系統、使用模糊方程對不確定的非線性系統進行建模、使用Z數字解決模糊方程以及使用Z數字解決模糊微分方程的數值解的研究結果。本書用清晰易懂的語言解釋了適用於現實情境的概念和原則，並提供了以下內容：

- 使用模糊方程和模糊微分方程對不確定的非線性系統進行建模和控制
- 為非專業人士提供不確定的非線性系統概述
- 教授讀者使用模擬、建模和驗證技能，這些技能對科學研究和工程系統開發非常有價值
- 通過插圖、表格、示例和模擬來加強理解

《使用模糊方程和Z數字對不確定的非線性系統進行建模和控制》適合作為高級學生、學術和工業研究人員以及系統工程、學習控制系統、神經網絡、計算智能和模糊邏輯控制領域的從業人員的教科書。

Wen Yu, PhD, is Professor at CINVESTAV-IPN (National Polytechnic Institute), Mexico City, Mexico. He is Associate Editor of IEEE Transactions on Cybernetics, Neurocomputing, and Journal of Intelligent and Fuzzy Systems. Dr. Yu is a member of the Mexican Academy of Sciences.

Raheleh Jafari is a postdoctoral research fellow at Centre for Artificial Intelligence Research (CAIR), University of Agder, Grimstad, Norway. She is on the editorial board of the Journal of Intelligent and Fuzzy Systems, and served as a reviewer in various journals and conferences. Her research interest is in the field of artificial intelligence, fuzzy control, machine learning, nonlinear systems, neural networks, and fuzzy engineering.

Wen Yu博士是墨西哥城CINVESTAV-IPN（國家理工學院）的教授。他是《IEEE Transactions on Cybernetics》、《Neurocomputing》和《Journal of Intelligent and Fuzzy Systems》的副編輯。Yu博士是墨西哥科學院的成員。

Raheleh Jafari是挪威格林斯塔德Agder大學人工智慧研究中心（CAIR）的博士後研究員。她是《Journal of Intelligent and Fuzzy Systems》的編輯委員會成員，並在各種期刊和會議上擔任審稿人。她的研究興趣涉及人工智慧、模糊控制、機器學習、非線性系統、神經網絡和模糊工程領域。