Zeroing Neural Networks

Describes the theoretical and practical aspects of finite-time ZNN methods for solving an array of computational problems

Zeroing Neural Networks (ZNN) have become essential tools for solving discretized sensor-driven time-varying matrix problems in engineering, control theory, and on-chip applications for robots. Building on the original ZNN model, finite-time zeroing neural networks (FTZNN) enables efficient, accurate, and predictive real-time computations. Setting up discretized FTZNN algorithms for different time-varying matrix problems require distinct steps.

Zeroing Neural Networks provides in-depth information on the finite-time convergence of ZNN models in solving computational problems. Divided into eight parts, this comprehensive resource covers modeling methods, theoretical analysis, computer simulations, nonlinear activation functions, and more. Each part focuses on a specific type of time-varying computational problem, such as the application of FTZNN to the Lyapunov equation, linear matrix equation, and matrix inversion. Throughout the book, tables explain the performance of different models, while numerous illustrative examples clarify the advantages of each FTZNN method. In addition, the book:

• Describes how to design, analyze, and apply FTZNN models for solving computational problems
• Presents multiple FTZNN models for solving time-varying computational problems
• Details the noise-tolerance of FTZNN models to maximize the adaptability of FTZNN models to complex environments
• Includes an introduction, problem description, design scheme, theoretical analysis, illustrative verification, application, and summary in every chapter

Zeroing Neural Networks: Finite-time Convergence Design, Analysis, and Applications is an essential resource for scientists, researchers, academic lecturers, and postgraduates in the field, as well as a valuable reference for engineers and other practitioners working in neurocomputing and intelligent control.

描述了有限時間零化神經網絡（FTZNN）方法在解決一系列計算問題中的理論和實踐方面。零化神經網絡（ZNN）已成為在工程、控制理論和機器人的芯片應用中解決離散化感測器驅動的時變矩陣問題的重要工具。在原始ZNN模型的基礎上，有限時間零化神經網絡（FTZNN）實現了高效、準確和預測性的實時計算。為不同時變矩陣問題設置離散化FTZNN算法需要不同的步驟。

《零化神經網絡》提供了有關ZNN模型在解決計算問題中的有限時間收斂的深入信息。本書分為八個部分，全面介紹了建模方法、理論分析、計算機模擬、非線性激活函數等內容。每個部分都專注於特定類型的時變計算問題，例如將FTZNN應用於李亞普諾夫方程、線性矩陣方程和矩陣求逆等。全書通過表格解釋了不同模型的性能，並通過大量的實例說明了每種FTZNN方法的優勢。此外，本書還包括：

- 描述如何設計、分析和應用FTZNN模型解決計算問題
- 提供多種FTZNN模型解決時變計算問題
- 詳細介紹FTZNN模型的噪聲容忍度，以最大程度地提高FTZNN模型對複雜環境的適應性
- 每章節都包括引言、問題描述、設計方案、理論分析、實例驗證、應用和總結

《零化神經網絡：有限時間收斂設計、分析和應用》是科學家、研究人員、學術講師和研究生的重要資源，也是神經計算和智能控制領域工程師和其他從業人員的寶貴參考資料。

LIN XIAO, PhD, is a Professor in the College of Information Science and Engineering at Hunan Normal University, Changsha, China. He has authored more than 100 papers in international conferences and journals, including IEEE-TCYB, IEEE-TII, IEEE-TSMCS. Professor Xiao is Associate Editor of IEEE-TNNLS.

LEI JIA is a PhD degree candidate in Operations Research and Control in the College of Mathematics and Statistics at Hunan Normal University, Changsha, China. She has authored or co-authored more than 20 scientific articles, including 13 IEEE-transaction papers.

LIN XIAO博士是中國長沙湖南師範大學資訊科學與工程學院的教授。他在國際會議和期刊上發表了100多篇論文，包括IEEE-TCYB、IEEE-TII和IEEE-TSMCS等。Xiao教授是IEEE-TNNLS的副編輯。

LEI JIA是中國長沙湖南師範大學數學與統計學院運籌學與控制專業的博士候選人。她已經撰寫或合著了20多篇科學文章，其中包括13篇IEEE交易論文。