Stability Analysis of Neural Networks and Evolving Intelligent Systems
暫譯: 神經網絡與演化智能系統的穩定性分析
Rubio, Jose de Jesus
- 出版商: Springer
- 出版日期: 2025-05-01
- 售價: $6,560
- 貴賓價: 9.5 折 $6,232
- 語言: 英文
- 頁數: 214
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 3031872819
- ISBN-13: 9783031872815
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商品描述
This book explores the stability analysis of neural networks and evolving intelligent systems, focusing on their ability to adapt to changing environments. It differentiates between neural networks, which have a static structure and dynamic parameter learning, and evolving intelligent systems, where both structure and parameters are dynamic. A key concern addressed is ensuring the stability of these systems, as instability can lead to damage or accidents in online applications. Stability Analysis of Neural Networks and Evolving Intelligent Systems emphasizes that stable algorithms used in these systems must be compact, effective, and stable.
The book is divided into two parts: the first five chapters cover stability analysis of neural networks, while the latter five chapters explore stability analysis of evolving intelligent systems. The Lyapunov method is the primary tool used for these analyses. Neural networks are applied to various modeling and prediction tasks, including warehouse load distribution, wind turbine behavior, crude oil blending, and beetle population dynamics. Evolving intelligent systems are applied to modeling brain and eye signals, nonlinear systems with dead-zone input, and the Box Jenkins furnace.
Each chapter introduces specific techniques and algorithms, such as a backpropagation algorithm with a time-varying rate for neural networks, analytic neural network models for wind turbines, and self-organizing fuzzy modified least square networks (SOFMLS) for evolving systems. The book also addresses challenges like incomplete data and big data learning, proposing hybrid methods and modified algorithms to improve performance and stability. The effectiveness of the proposed techniques is verified through simulations and comparisons with existing methods.
商品描述(中文翻譯)
這本書探討神經網絡和演變智能系統的穩定性分析，重點在於它們適應變化環境的能力。它區分了神經網絡，這些網絡具有靜態結構和動態參數學習，以及演變智能系統，這些系統的結構和參數都是動態的。一個主要的關注點是確保這些系統的穩定性，因為不穩定可能導致在線應用中的損壞或事故。《神經網絡與演變智能系統的穩定性分析》強調，這些系統中使用的穩定算法必須是緊湊的、有效的和穩定的。
本書分為兩個部分：前五章涵蓋神經網絡的穩定性分析，而後五章則探討演變智能系統的穩定性分析。Lyapunov 方法是這些分析的主要工具。神經網絡應用於各種建模和預測任務，包括倉庫負載分配、風力發電機行為、原油混合和甲蟲種群動態。演變智能系統則應用於建模大腦和眼睛信號、具有死區輸入的非線性系統，以及Box Jenkins爐。
每一章介紹特定的技術和算法，例如用於神經網絡的時間變化率的反向傳播算法、風力發電機的解析神經網絡模型，以及用於演變系統的自組織模糊修正最小二乘網絡（SOFMLS）。本書還解決了不完整數據和大數據學習等挑戰，提出混合方法和修正算法以提高性能和穩定性。所提出技術的有效性通過模擬和與現有方法的比較進行驗證。
作者簡介
Jose de Jesus Rubio is a full time professor of the Sección de Estudios de Posgrado e Investigación, ESIME Azcapotzalco, Instituto Politécnico Nacional. He has published over 183 international journal papers with 4130 cites from Scopus. He has been Senior Editor of IEEE Transactions on Neural Networks and Learning Systems. He has been Associate Editor of IEEE Transactions on Fuzzy Systems, Neural Networks, Neural Computing & Applications, Frontiers in Neurorobotics. He has been Guest Editor of Neurocomputing, Applied Soft Computing, Journal of Supercomputing, Mathematics, Sensors, Machines, Computational Intelligence and Neuroscience, Frontiers in Psychology, Journal of Real-Time Image Processing, Computer Science and Information Systems. He has been Tutor of 4 P.Ph.D. students, 27 Ph.D. students, 48 M.S. students, 4 S. students, and 17 B.S. students. His fields of interest are robotic systems, energy systems, modeling, intelligent systems, control.
作者簡介(中文翻譯)
Jose de Jesus Rubio 是國立 Polytechnic Institute 的 ESIME Azcapotzalco 研究所的全職教授。他已在國際期刊上發表超過 183 篇論文，並在 Scopus 上獲得 4130 次引用。他曾擔任《IEEE 神經網絡與學習系統期刊》的高級編輯，並擔任《IEEE 模糊系統期刊》、《神經網絡》、《神經計算與應用》、《神經機器人學前沿》的副編輯。他還擔任過《神經計算》、《應用軟計算》、《超級計算期刊》、《數學》、《傳感器》、《機器》、《計算智能與神經科學》、《心理學前沿》、《即時影像處理期刊》、《計算機科學與資訊系統》的客座編輯。他指導過 4 位 P.Ph.D. 學生、27 位 Ph.D. 學生、48 位碩士生、4 位 S. 學生和 17 位學士生。他的研究領域包括機器人系統、能源系統、建模、智能系統和控制。
