A sliding-window modeling approach of neural network (SWMANN) was presented. The basic idea is that training data for neural network should be reconstructed by means of a slide-window way to build input and target samples. It means that the output is determined not only by the current input, but also by the past input, which could better follow the dynamic change and development of real systems. In this paper, SWMANN was introduced. A detailed theoretical derivation was described for its implementation by taking wavelet neural network as a modeling tool. Moreover, SWMANN was compared with classical modeling approach of neural network (CMANN). Theoretical results indicated that CMANN is a special case of SWMANN when sliding-window parameters are selected as 1. Therefore, compared with CMANN, SWMANN presented in this paper is a more general form.
목차
Abstract 1. Introduction 2. Description of SWMANN 3. Implementation of SWMANN 3.1. Wavelet Neural Network with CMANN 3.2. Wavelet Neural Network with SWMANN 4. Modeling and Predicting Dynamical Systems with SWMANN 5. Discussions 6. Conclusions References
키워드
neural networksliding-window modelingwaveletdynamical system
저자
Xiao Laisheng [ Guangdong Ocean University, Zhanjiang, 524088, China ]
보안공학연구지원센터(IJCA) [Science & Engineering Research Support Center, Republic of Korea(IJCA)]
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 Control and Automation
간기
월간
pISSN
2005-4297
수록기간
2008~2016
십진분류
KDC 505DDC 605
이 권호 내 다른 논문 / International Journal of Control and Automation Vol.7 No.8