The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
이 조사에서는 적응형 퍼지 신경 관찰자(AFNO)를 적용하여 스칼라 전송 신호만을 통해서 알려지지 않은 혼돈 시스템 클래스를 동기화합니다. 비선형 카오스 시스템이 미분기하학 방법에 의해 Lur'e 시스템 유형의 표준 형태로 변환될 수 있는 경우 제안된 방법을 동기화하여 사용할 수 있습니다. 이 접근 방식에서는 AFNO의 적응형 퍼지 신경망(FNN)이 온라인으로 채택되어 송신기의 비선형 항을 모델링합니다. 또한 마스터의 알려지지 않은 상태는 슬레이브 측의 관찰자 설계를 사용하여 전송된 하나의 상태에서 재구성될 수 있습니다. 모든 상태가 관찰되면 동기화가 이루어집니다. 활용된 방식은 송신기가 다른 카오스 시스템으로 변경되더라도 온라인으로 송신기 상태를 적응적으로 추정할 수 있습니다. 반면, AFNO는 모델링 오류와 외부 경계 교란에 대해 견고성을 보장할 수 있습니다. 시뮬레이션 결과는 AFNO 설계가 혼돈 동기화 적용에 유효하다는 것을 확인시켜 줍니다.
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부
Bing-Fei WU, Li-Shan MA, Jau-Woei PERNG, "Observer-Based Synchronization for a Class of Unknown Chaos Systems with Adaptive Fuzzy-Neural Network" in IEICE TRANSACTIONS on Fundamentals,
vol. E91-A, no. 7, pp. 1797-1805, July 2008, doi: 10.1093/ietfec/e91-a.7.1797.
Abstract: This investigation applies the adaptive fuzzy-neural observer (AFNO) to synchronize a class of unknown chaotic systems via scalar transmitting signal only. The proposed method can be used in synchronization if nonlinear chaotic systems can be transformed into the canonical form of Lur'e system type by the differential geometric method. In this approach, the adaptive fuzzy-neural network (FNN) in AFNO is adopted on line to model the nonlinear term in the transmitter. Additionally, the master's unknown states can be reconstructed from one transmitted state using observer design in the slave end. Synchronization is achieved when all states are observed. The utilized scheme can adaptively estimate the transmitter states on line, even if the transmitter is changed into another chaos system. On the other hand, the robustness of AFNO can be guaranteed with respect to the modeling error, and external bounded disturbance. Simulation results confirm that the AFNO design is valid for the application of chaos synchronization.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e91-a.7.1797/_p
부
@ARTICLE{e91-a_7_1797,
author={Bing-Fei WU, Li-Shan MA, Jau-Woei PERNG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Observer-Based Synchronization for a Class of Unknown Chaos Systems with Adaptive Fuzzy-Neural Network},
year={2008},
volume={E91-A},
number={7},
pages={1797-1805},
abstract={This investigation applies the adaptive fuzzy-neural observer (AFNO) to synchronize a class of unknown chaotic systems via scalar transmitting signal only. The proposed method can be used in synchronization if nonlinear chaotic systems can be transformed into the canonical form of Lur'e system type by the differential geometric method. In this approach, the adaptive fuzzy-neural network (FNN) in AFNO is adopted on line to model the nonlinear term in the transmitter. Additionally, the master's unknown states can be reconstructed from one transmitted state using observer design in the slave end. Synchronization is achieved when all states are observed. The utilized scheme can adaptively estimate the transmitter states on line, even if the transmitter is changed into another chaos system. On the other hand, the robustness of AFNO can be guaranteed with respect to the modeling error, and external bounded disturbance. Simulation results confirm that the AFNO design is valid for the application of chaos synchronization.},
keywords={},
doi={10.1093/ietfec/e91-a.7.1797},
ISSN={1745-1337},
month={July},}
부
TY - JOUR
TI - Observer-Based Synchronization for a Class of Unknown Chaos Systems with Adaptive Fuzzy-Neural Network
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1797
EP - 1805
AU - Bing-Fei WU
AU - Li-Shan MA
AU - Jau-Woei PERNG
PY - 2008
DO - 10.1093/ietfec/e91-a.7.1797
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E91-A
IS - 7
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - July 2008
AB - This investigation applies the adaptive fuzzy-neural observer (AFNO) to synchronize a class of unknown chaotic systems via scalar transmitting signal only. The proposed method can be used in synchronization if nonlinear chaotic systems can be transformed into the canonical form of Lur'e system type by the differential geometric method. In this approach, the adaptive fuzzy-neural network (FNN) in AFNO is adopted on line to model the nonlinear term in the transmitter. Additionally, the master's unknown states can be reconstructed from one transmitted state using observer design in the slave end. Synchronization is achieved when all states are observed. The utilized scheme can adaptively estimate the transmitter states on line, even if the transmitter is changed into another chaos system. On the other hand, the robustness of AFNO can be guaranteed with respect to the modeling error, and external bounded disturbance. Simulation results confirm that the AFNO design is valid for the application of chaos synchronization.
ER -