A Fuzzy-Model-Based Chaotic Synchronization and Its Implementation on a Secure Communication System

This paper presents a modulated T–S fuzzy model that can be applied to both continuous/discrete chaotic synchronization, chaotic model following and secure communication. The modulated T–S fuzzy model is constructed by choosing the common factor or the only variable of nonlinear terms in chaotic systems as the premise variable of fuzzy rules and output signal. Following this model, fuzzy chaotic synchronization is achieved by utilizing linear matrix inequalities design and some restricting conditions can be relaxed. This simplified design framework can be applied to many well-known chaotic systems. Also, for chaotic communications, this modulated T–S fuzzy model illustrates asymptotical recovering of the message. Moreover, the usage of combinational states as the coupling signal for Luré type systems is taken into consideration. In this case, the receiver design for synchronization and secure communication is also proposed. The simulation results are shown with expected satisfactory performance.

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The Model of Super-Chen Chaotic Scheme with Oscillating Parameters under the Impulsive Control

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.282-283.608 ◽

2011 ◽

Vol 282-283 ◽

pp. 608-611

Author(s):

Ying Kui Li

Keyword(s):

Chaotic System ◽

Communication System ◽

Secure Communication ◽

Impulsive Control ◽

Chaotic Synchronization ◽

Control Signal ◽

Control Methods ◽

Security Risks ◽

Two Systems ◽

Synchronization Scheme

The control of chaotic synchronization is the kernel technology in chaos-based secure communication. Those control methods have to transmitting control signal which increase the security risks of the communication system. Attacker can reconstruct the chaotic system or estimate parameters by using the information of the chaotic system. In this paper we propose a hybrid Super Chen chaotic synchronization scheme control which contains both continuous chaotic system with oscillating parameters and discrete chaotic system. If oscillating parameters satisfyingwhen , we proved that two systems can get synchronized without control signal transmitting.

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Chaotic Synchronization Using Sampled-Data Fuzzy Controller Based on Fuzzy-Model-Based Approach

IEEE Transactions on Circuits and Systems I Regular Papers ◽

10.1109/tcsi.2008.916413 ◽

2008 ◽

Vol 55 (3) ◽

pp. 883-892 ◽

Cited By ~ 39

Author(s):

H.K. Lam ◽

L.D. Seneviratne

Keyword(s):

Fuzzy Controller ◽

Fuzzy Model ◽

Chaotic Synchronization ◽

Sampled Data ◽

Model Based

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SECURE COMMUNICATIONS VIA CHAOTIC SYNCHRONIZATION II: NOISE REDUCTION BY CASCADING TWO IDENTICAL RECEIVERS

International Journal of Bifurcation and Chaos ◽

10.1142/s0218127493001070 ◽

1993 ◽

Vol 03 (05) ◽

pp. 1319-1325 ◽

Cited By ~ 33

Author(s):

RENÉ LOZI ◽

LEON O. CHUA

Keyword(s):

Noise Reduction ◽

Communication System ◽

Secure Communication ◽

Computer Experiments ◽

Original System ◽

Chaotic Synchronization ◽

Secure Communications ◽

Original Message

The signal recovered from the first reported experimental secure communication system via chaotic synchronization contains some inevitable noise which degrades the fidelity of the original message. In this letter we show by computer experiments that this noise can be significantly reduced by cascading the output of the receiver in the original system into an identical copy of this receiver.

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Design and Numerical Simulation of Unidirectional Chaotic Synchronization and its Application in Secure Communication System

Journal of Engineering Science and Technology Review ◽

10.25103/jestr.064.08 ◽

2013 ◽

Vol 6 (4) ◽

pp. 66-73 ◽

Cited By ~ 5

Author(s):

A. Sambas ◽

◽

M. Sanjaya W. S. ◽

M. Mamat ◽

◽

...

Keyword(s):

Numerical Simulation ◽

Communication System ◽

Secure Communication ◽

Chaotic Synchronization

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Fuzzy Modeling to Simulate Surface Roughness in CNC Turning of AISI 1045 Steel

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.486.262 ◽

2011 ◽

Vol 486 ◽

pp. 262-265

Author(s):

Amit Kohli ◽

Mudit Sood ◽

Anhad Singh Chawla

Keyword(s):

Experimental Data ◽

Surface Roughness ◽

Process Parameters ◽

Fuzzy Model ◽

Fuzzy Modeling ◽

Cnc Machine ◽

Model Based ◽

Aisi 1045 Steel ◽

Aisi 1045 ◽

1045 Steel

The objective of the present work is to simulate surface roughness in Computer Numerical Controlled (CNC) machine by Fuzzy Modeling of AISI 1045 Steel. To develop the fuzzy model; cutting depth, feed rate and speed are taken as input process parameters. The predicted results are compared with reliable set of experimental data for the validation of fuzzy model. Based upon reliable set of experimental data by Response Surface Methodology twenty fuzzy controlled rules using triangular membership function are constructed. By intelligent model based design and control of CNC process parameters, we can enhance the product quality, decrease the product cost and maintain the competitive position of steel.

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