Discrete-Time Neural Observers: Analysis and Applications
暫譯: 離散時間神經觀察器:分析與應用

Alma Y. Alanis, Edgar N Sanchez

  • 出版商: Academic Press
  • 出版日期: 2017-02-08
  • 售價: $5,780
  • 貴賓價: 9.5$5,491
  • 語言: 英文
  • 頁數: 150
  • 裝訂: Paperback
  • ISBN: 0128105437
  • ISBN-13: 9780128105436
  • 海外代購書籍(需單獨結帳)

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商品描述

Discrete-Time Neural Observers: Analysis and Applications presents recent advances in the theory of neural state estimation for discrete-time unknown nonlinear systems with multiple inputs and outputs. The book includes rigorous mathematical analyses, based on the Lyapunov approach, that guarantee their properties. In addition, for each chapter, simulation results are included to verify the successful performance of the corresponding proposed schemes.

In order to complete the treatment of these schemes, the authors also present simulation and experimental results related to their application in meaningful areas, such as electric three phase induction motors and anaerobic process, which show the applicability of such designs. The proposed schemes can be employed for different applications beyond those presented.

The book presents solutions for the state estimation problem of unknown nonlinear systems based on two schemes. For the first one, a full state estimation problem is considered; the second one considers the reduced order case with, and without, the presence of unknown delays. Both schemes are developed in discrete-time using recurrent high order neural networks in order to design the neural observers, and the online training of the respective neural networks is performed by Kalman Filtering.

  • Presents online learning for Recurrent High Order Neural Networks (RHONN) using the Extended Kalman Filter (EKF) algorithm
  • Contains full and reduced order neural observers for discrete-time unknown nonlinear systems, with and without delays
  • Includes rigorous analyses of the proposed schemes, including the nonlinear system, the respective observer, and the Kalman filter learning
  • Covers real-time implementation and simulation results for all the proposed schemes to meaningful applications

商品描述(中文翻譯)

《離散時間神經觀測器:分析與應用》介紹了針對具有多輸入和多輸出的離散時間未知非線性系統的神經狀態估計理論的最新進展。本書包含基於李雅普諾夫方法的嚴謹數學分析,以保證其性質。此外,每一章節都包含模擬結果,以驗證相應提出方案的成功表現。

為了完整處理這些方案,作者還展示了與其在有意義領域(如電動三相感應馬達和厭氧過程)應用相關的模擬和實驗結果,顯示這些設計的適用性。所提出的方案可以應用於超出所展示的不同應用。

本書針對未知非線性系統的狀態估計問題提出了基於兩種方案的解決方案。第一種方案考慮完整狀態估計問題;第二種方案考慮有無未知延遲的降階情況。這兩種方案均在離散時間中使用遞歸高階神經網絡來設計神經觀測器,並通過卡爾曼濾波進行相應神經網絡的在線訓練。

- 提供使用擴展卡爾曼濾波器(EKF)算法的遞歸高階神經網絡(RHONN)在線學習
- 包含針對離散時間未知非線性系統的完整和降階神經觀測器,考慮有無延遲
- 包括對所提出方案的嚴謹分析,包括非線性系統、相應觀測器和卡爾曼濾波學習
- 涵蓋所有提出方案的實時實現和模擬結果,應用於有意義的應用領域