• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.93.1-93
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• 2002

In this paper, an efficient robust control method for chaotic system introducing the concept, the edge of chaos (:boundary status between chaos and non-chaos), is proposed. To realize this concept, we introduce an extended performance index which consists of two parts. One is for achievement of the system's objects, another is for keeping the system edge of chaos. Parameters of the neural network controller are adjusted to minimize the value of the extended performance index and achieve the above two objects using Random...

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.4
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• pp.242-248
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• 2007

This paper analyzes on the chaos characteristics of the chaotic neural networks and presents the convergence condition. Although the transient chaos of neural network sould be beneficial to overcome the local minimum problem and speed up the learning, the permanent chaotic response gives adverse effect on optimization problems and makes neural network unstable in general. This paper investigates the dynamic characteristics of the chaotic neural networks with the chaotic dynamic neuron, and presents the convergence condition for stabilizing the chaotic neural networks.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1009-1012
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• 1993

An association memory, solving an optimization problem, a Boltzmann machine scheme learning and a back propagation learning in our chaos neuron models are reviewed and some new results are presented. In each model its microscopicrule (a parameter of a chaos system in a neuron) is subject to its macroscopic state. This feedback and chaos dynamics are essential mechanisms of our model and their roles are briefly discussed.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.4
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• pp.396-401
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• 2006

It is well-known that a neural network with cyclic connections generates plural limit cycles, thus, being used as a memory system for storing large number of dynamic information. In this paper, a continuous-time cyclic connection neural network was built so that each neuron is connected only to its nearest neurons with binary synaptic weights of ${\pm}1$. The type and the number of limit cycles generated by such network has also been demonstrated through simulation. In particular, the effect of chaos signal for transition between limit cycles has been tested. Furthermore, it is evaluated whether the chaotic noise is more effective than random noise in the process of the dynamical neural networks.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.1
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• pp.91-96
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• 2012

Recently, the research of security and its related problems has been received great interested. The research for hper-chaos systems and its synchronization are actively processing as one of method to apply to secure and cryptography communication. In this paper, we propose the synchronization method by coupling coefficient of linear and nonlinear in order to accomplish the synchronization of hyper-chaos system that organized by SC-CNN(State-Controlled Cellular Neural Network). We also verify and confirm the result of synchronization between entire transmitter and receiver, and each subsystem in transmitter and receiver through the phase portrait and difference of time-series by the computer simulation.

• Journal of information and communication convergence engineering
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• v.1 no.4
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• pp.217-222
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• 2003

In this paper, we introduce a hyper-chaos synchronization method using hyper-chaos circuit consist of State-Controlled Cellular Neural Network (SC-CNN). We make a hyper-chaos circuit using SC-CNN with the n-double scroll. A hyper-chaos circuit is created by applying identical n-double scroll or non-identical n-double scroll and Chua's oscillator with weak coupled method to each cell. Hyper-chaos synchronization was achieved using GS(Generalized Synchronization) method between the transmitter and receiver about each state variable in the SC-CNN.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.575-578
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• 2003

In this paper, we introduce a hyper-chaos secure communication method using Hyper-chaos consist of State-Controlled Cellular Neural Network (SC-CNN). A hyper-chaos circuit is created by applying identical n-double scroll with weak coupled method to each cell. Hyper-chaos synchronization was achieved using embedding synchronization between the transmitter and receiver about in SC CNN. And then, we accomplish secure communication by synthesizing the desired information with a hyper-chaos circuit by embedding the information signal to the only one state variable instead of all state variables in the driven-synchronization method. After transmitting the synthesized signal to the identical channel, we confirm secure communication by separating the information signal and the hyper-chaos signal in the receiver.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2521-2525
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• 2005

In this paper, we presented a predictive control technique, which is based on wavelet neural network (WNN), for the control of chaotic systems whose precise mathematical models are not available. The WNN is motivated by both the multilayer feedforward neural network definition and wavelet decomposition. The wavelet theory improves the convergence of neural network. In order to design predictive controller effectively, the WNN is used as the predictor whose parameters are tuned by error between the output of actual plant and the output of WNN. Also the training method for the finding a good WNN model is the Extended Kalman algorithm which updates network parameters to converge to the reference signal during a few iterations. The benefit of EKF training method is that the WNN model can have better accuracy for the unknown plant. Finally, through computer simulations, we confirmed the performance of the proposed control method.

• Journal of information and communication convergence engineering
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• v.1 no.4
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• pp.223-228
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• 2003

In this paper, we introduce a hyper-chaos secure communication method using hyper-chaos circuit onsist of State-Controlled Cellular Neural Network SC-CNN). We make a hyper-chaos circuit using SC-CNN with the n-double scroll or Chua's oscillator. A hyper-chaos circuit is created by applying identical n-double scroll or non-identical n-double scroll and Chua's oscillator with weak coupled method to each cell. Hyper-chaos ynchronization was achieved using GS (Generalized Synchronization) method between the transmitter and receiver about each state variable in the SC-CNN. In order to secure communication, we have synthesizing the desired information with a hyper-chaos circuit by adding the information signal to the hyper-chaos signal using the SC-CNN in the transmitter. And then, transmitting the synthesized signal to the ideal channel, we confirm secure communication by separating the information signal and the hyper-chaos signal in the receiver.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.504-507
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• 2003

In this paper, we introduce a new hyper-chaos synchronization method called embedding synchronization using hyper-chaos consist of State-Controlled Cellular Neural Network (SC-CNN). We make a hyper-chaos circuit using SC-CNN with the n-double scroll. A hyper-chaos circuit is created by applying identical n-double scroll with weak coupled method to each cell. Hyper-chaos synchronization was achieved using embedding synchronization between the transmitter and receiver about each state variable in the SC-CNN.