The proposed RAITIIFNNC system is comprised of a interval type II fuzzy neural network identifier and a robust controller. The identifier is utilized for online estimation of the compound uncertainties. The robust controller is used to attenuate the effects of the approximation error so that the perfect tracking and synchronization of chaotic systems are achieved. All the parameter learning algorithms are derived based on Lyapunov stability theorem to ensure network convergence as well as stable synchronization performance. From the simulation example, to synchronize two Lorenz chaotic systems, it has been shown that the effectiveness of the proposed method has been verified.
목차
Abstract 1. Introduction 2. Problem Formulation 3. Structure Learning Algorithm of ITIIFNN 4. Design of the RAITIIFNN Identifier and Robust Controller 5. Computer Simulation 6. Conclusion References
키워드
Uncertain chaotic systemsChaos synchronizationAdaptive controlInterval type II fuzzy neural networkLyapunov stability
저자
Xiang-jian Chen [ Jiangsu University of Science and Technology, School of Computer Science and Engineering, China ]
Di Li [ China Shipbuilding Industry Corporation 723, China. ]
보안공학연구지원센터(IJSIP) [Science & Engineering Research Support Center, Republic of Korea(IJSIP)]
설립연도
2006
분야
공학>컴퓨터학
소개
1. 보안공학에 대한 각종 조사 및 연구
2. 보안공학에 대한 응용기술 연구 및 발표
3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최
4. 보안공학 기술의 상호 협조 및 정보교환
5. 보안공학에 관한 표준화 사업 및 규격의 제정
6. 보안공학에 관한 산학연 협동의 증진
7. 국제적 학술 교류 및 기술 협력
8. 보안공학에 관한 논문지 발간
9. 기타 본 회 목적 달성에 필요한 사업
간행물
간행물명
International Journal of Signal Processing, Image Processing and Pattern Recognition
간기
격월간
pISSN
2005-4254
수록기간
2008~2016
십진분류
KDC 505DDC 605
이 권호 내 다른 논문 / International Journal of Signal Processing, Image Processing and Pattern Recognition Vol.8 No.12