Detailed Information
Cited 0 time in 
Cited 0 time in 
Cited 0 time in
Metadata Downloads
Receding horizon disturbance attenuation for Takagi-Sugeno fuzzy switched dynamic neural networks
- Authors
- Ahn, Choon Ki
- Issue Date
- 1-Oct-2014
- Publisher
- ELSEVIER SCIENCE INC
- Keywords
- Neuro-fuzzy system; Fuzzy system model; Switched neural network; Receding horizon disturbance attenuator (RHDA); Linear matrix inequality (LMI)
- Citation
- INFORMATION SCIENCES, v.280, pp.53 - 63
- Indexed
- SCIE
SCOPUS
- Journal Title
- INFORMATION SCIENCES
- Volume
- 280
- Start Page
- 53
- End Page
- 63
- ISSN
- 0020-0255
- Abstract
- In this paper, we propose a new receding horizon disturbance attenuator (RHDA) for Takagi-Sugeno (T-S) fuzzy switched Hopfield neural networks with external disturbance. First, a new set of linear matrix inequality (LMI) conditions is proposed for the finite terminal weighting matrix of the receding horizon cost function with a cross term. Second, under this condition, we show that the proposed RHDA attenuates the effect of external disturbance on T-S fuzzy switched Hopfield neural networks with a guaranteed infinite horizon H-infinity performance. In addition, we prove that the proposed RHDA guarantees internal stability in closed-loop systems. A numerical example is presented to describe the effectiveness of the proposed RHDA scheme. (C) 2014 Elsevier Inc. All rights reserved.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - College of Engineering > School of Electrical Engineering > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
Related Researcher
- Ahn, Choon ki
- College of Engineering (School of Electrical Engineering)