Lag synchronization of unknown chaotic delayed Yang-Yang-type fuzzy neural networks with noise perturbation based on adaptive control and parameter identification

Authors:
Yonghui Xia;Zijiang Yang;Maoan Han
Affiliations:
Department of Mathematics, Zhejiang Normal University, Jinhua, China;School of Information Technology, York University, Toronto, ON, Canada;Institute of Mathematics, Shanghai Normal University, Shanghai, China
Venue:
IEEE Transactions on Neural Networks
Year:
2009

Citing 4
Cited 12

Exponential stability and periodic solutions of fuzzy cellular neural networks with time-varying delays

Neurocomputing
Impulsive Effects on Stability of Fuzzy Cohen–Grossberg Neural Networks With Time-Varying Delays

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robust Control for Networked Systems With Random Packet Losses

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Stability analysis for stochastic Cohen-Grossberg neural networks with mixed time delays

IEEE Transactions on Neural Networks

Existence and global exponential stability of periodic solution for delayed neural networks with impulsive and stochastic effects

Neurocomputing
Existence and global attractivity of almost periodic solutions for delayed high-ordered neural networks

Neurocomputing
Impulsive effect on the delayed Cohen-Grossberg-type BAM neural networks

Neurocomputing
Exponential H∞synchronization of general discrete-time chaotic neural networks with or without time delays

IEEE Transactions on Neural Networks
Continuous attractors of Lotka-Volterra recurrent neural networks with infinite neurons

IEEE Transactions on Neural Networks
Cluster synchronization for delayed Lur'e dynamical networks based on pinning control

Neurocomputing
Grey-prediction self-organizing fuzzy controller for robotic motion control

Information Sciences: an International Journal
Exponential and almost sure exponential stability of stochastic fuzzy delayed Cohen-Grossberg neural networks

Fuzzy Sets and Systems
Intelligent control of a constant turning force system with fixed metal removal rate

Applied Soft Computing
Exponential synchronization of coupled fuzzy neural networks with disturbances and mixed time-delays

Neurocomputing
Exponential Synchronization of Stochastic Fuzzy Cellular Neural Networks with Reaction-Diffusion Terms via Periodically Intermittent Control

Neural Processing Letters
Design of a grey-prediction self-organizing fuzzy controller for active suspension systems

Applied Soft Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper considers the lag synchronization (LS) issue of unknown coupled chaotic delayed Yang-Yang-type fuzzy neural networks (YYFCNN) with noise perturbation. Separate research work has been published on the stability of fuzzy neural network and LS issue of unknown coupled chaotic neural networks, as well as its application in secure communication. However, there have not been any studies that integrate the two. Motivated by the achievements from both fields, we explored the benefits of integrating fuzzy logic theories into the study of LS problems and applied the findings to secure communication. Based on adaptive feedback control techniques and suitable parameter identification, several sufficient conditions are developed to guarantee the LS of coupled chaotic delayed YYFCNN with or without noise perturbation. The problem studied in this paper is more general in many aspects. Various problems studied extensively in the literature can be treated as special cases of the findings of this paper, such as complete synchronization (CS), effect of fuzzy logic, and noise perturbation. This paper presents an illustrative example and uses simulated results of this example to show the feasibility and effectiveness of the proposed adaptive scheme. This research also demonstrates the effectiveness of application of the proposed adaptive feedback scheme in secure communication by comparing chaotic masking with fuzziness with some previous studies. Chaotic signal with fuzziness is more complex, which makes unmasking more difficult due to the added fuzzy logic.