| 000 | 00000cam u22002054a 4500 | |
| 001 | 000000821033 | |
| 005 | 20230224170251 | |
| 008 | 010720s2001 nyua b 001 0 eng | |
| 010 | ▼a 1049240 | |
| 015 | ▼a GBA2-04523 | |
| 020 | ▼a 0471369985 (alk. paper) | |
| 040 | ▼a DLC ▼c DLC ▼d UKM ▼d 211009 | |
| 042 | ▼a pcc | |
| 049 | 1 | ▼l 121080918 ▼f 과학 |
| 050 | 0 0 | ▼a QA76.87 ▼b .K35 2001 |
| 082 | 0 0 | ▼a 006.3/2 ▼2 21 |
| 090 | ▼a 006.32 ▼b K14 | |
| 245 | 0 0 | ▼a Kalman filtering and neural networks / ▼c edited by Simon Haykin. |
| 260 | ▼a New York : ▼b Wiley, ▼c c2001. | |
| 300 | ▼a xiii, 284 p. : ▼b ill. ; ▼c 24 cm. | |
| 440 | 0 | ▼a Adaptive and learning systems for signal processing, communications, and control |
| 500 | ▼a "A Wiley Interscience publication." | |
| 504 | ▼a Includes bibliographical references and index. | |
| 650 | 0 | ▼a Neural networks (Computer science). |
| 650 | 0 | ▼a Kalman filtering. |
| 700 | 1 | ▼a Haykin, Simon S., ▼d 1931- ▼0 AUTH(211009)149760. |
소장정보
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 과학도서관/Sci-Info(2층서고)/ | 청구기호 006.32 K14 | 등록번호 121080918 (19회 대출) | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
| No. 2 | 소장처 세종학술정보원/과학기술실(5층)/ | 청구기호 006.32 K14 | 등록번호 151181850 (2회 대출) | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 과학도서관/Sci-Info(2층서고)/ | 청구기호 006.32 K14 | 등록번호 121080918 (19회 대출) | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 세종학술정보원/과학기술실(5층)/ | 청구기호 006.32 K14 | 등록번호 151181850 (2회 대출) | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
컨텐츠정보
책소개
State-of-the-art coverage of Kalman filter methods for the design of neural networks
This self-contained book consists of seven chapters by expert contributors that discuss Kalman filtering as applied to the training and use of neural networks. Although the traditional approach to the subject is almost always linear, this book recognizes and deals with the fact that real problems are most often nonlinear.
The first chapter offers an introductory treatment of Kalman filters with an emphasis on basic Kalman filter theory, Rauch-Tung-Striebel smoother, and the extended Kalman filter. Other chapters cover:
- An algorithm for the training of feedforward and recurrent multilayered perceptrons, based on the decoupled extended Kalman filter (DEKF)
- Applications of the DEKF learning algorithm to the study of image sequences and the dynamic reconstruction of chaotic processes
- The dual estimation problem
- Stochastic nonlinear dynamics: the expectation-maximization (EM) algorithm and the extended Kalman smoothing (EKS) algorithm
- The unscented Kalman filter
Each chapter, with the exception of the introduction, includes illustrative applications of the learning algorithms described here, some of which involve the use of simulated and real-life data. Kalman Filtering and Neural Networks serves as an expert resource for researchers in neural networks and nonlinear dynamical systems.
Die Kalman-Filterung ist ein wichtiges Spezialgebiet der Steuerungstechnik und Signalverarbeitung und die hochstentwickelte Methode fur das Design neuronaler Netze. Der unkonventionelle, nichtlineare Ansatz tragt der Tatsache Rechnung, dass in der Praxis meist nichtlineare Probleme von Bedeutung sind. Besprochen werden wichtige Anwendungen, zum Beispiel aus der Steuerungstechnik und der Finanzmathematik.
New feature
State-of-the-art coverage of Kalman filter methods for the design of neural networksThis self-contained book consists of seven chapters by expert contributors that discuss Kalman filtering as applied to the training and use of neural networks. Although the traditional approach to the subject is almost always linear, this book recognizes and deals with the fact that real problems are most often nonlinear.
The first chapter offers an introductory treatment of Kalman filters with an emphasis on basic Kalman filter theory, Rauch-Tung-Striebel smoother, and the extended Kalman filter. Other chapters cover:
* An algorithm for the training of feedforward and recurrent multilayered perceptrons, based on the decoupled extended Kalman filter (DEKF)
* Applications of the DEKF learning algorithm to the study of image sequences and the dynamic reconstruction of chaotic processes
* The dual estimation problem
* Stochastic nonlinear dynamics: the expectation-maximization (EM) algorithm and the extended Kalman smoothing (EKS) algorithm
* The unscented Kalman filter
Each chapter, with the exception of the introduction, includes illustrative applications of the learning algorithms described here, some of which involve the use of simulated and real-life data. Kalman Filtering and Neural Networks serves as an expert resource for researchers in neural networks and nonlinear dynamical systems.
정보제공 :
목차
Preface.
Contributors.
Kalman Filters (S. Haykin).
Parameter-Based Kalman Filter Training: Theory and Implementaion (G. Puskorius and L. Feldkamp).
Learning Shape and Motion from Image Sequences (G. Patel, et al.).
Chaotic Dynamics (G. Patel and S. Haykin).
Dual Extended Kalman Filter Methods (E. Wan and A. Nelson).
Learning Nonlinear Dynamical System Using the Expectation-Maximization Algorithm (S. Roweis and Z. Ghahramani).
The Unscencted Kalman Filter (E. Wan and R. van der Merwe).
Index.
정보제공 :