• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.4
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• pp.273-278
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• 2016

In this paper, chaotic circuit of the Lorentz system using multipliers, operational amplifiers, capacitor, fixed resistor and variable resistor for control has been designed in a electronic circuit. Through PSPICE program, electrical characteristics such as time waveforms, frequency spectra and phase attractors analyzed. And in the special area ($10{\sim}100k{\Omega}$) of the $500k{\Omega}$ control variable resistor, the circuit showed chaotic dynamics. Also, we implemented the circuit in a electronic hardware system with discrete elements. Measured results of the circuit coincided with simulated data.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.8
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• pp.3976-3982
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• 2013

This paper presents a temperature analysis of the chaotic behavior in the voltage controlled CMOS chaotic circuit. The circuit is based on a simple nonlinear function block which is needed for chaotic signal generation. It consists of a NFB (nonlinear function block), a level shifter and non-overlapping two-phase clock for sample and hold. By SPICE simulation, chaotic dynamics such as frequency spectra and bifurcations according to the temperature variations were analyzed. And, it was showed that the circuit can generate discrete chaotic signals within control voltage in the range from 1.2 V to 2.3 V in a specific temperature condition of $25^{\circ}C$.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2002.06a
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• pp.361-366
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• 2002

It has been reported that neural network with cyclic connections generates limit cycles. The dynamics of discrete time network with cyclic connections has been analyzed. But the dynamics of cyclic network in continuous time has not been known well due to its huge calculation complexity. In this paper, we study the dynamics of the continuous time network with cyclic connections and the effect of chaotic signal in the network for transitions between limit cycles.

• Journal of Korea Society of Industrial Information Systems
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• v.7 no.4
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• pp.22-28
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• 2002

It has been reported that neural network with cyclic connections generates limit cycles. The dynamics of discrete time network with cyclic connections has been analyzed. But the dynamics of cyclic network in continuous time has not been known well due to its huge calculation complexity. In this paper, we study the dynamics of the continuous time network with cyclic connections and the effect of chaotic signal in the network for transitions between limit cycles.

• Journal of Biomedical Engineering Research
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• v.15 no.2
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• pp.151-158
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• 1994

It has been shown that many of physiological systems have nonlinear dynamics. The evidences of these nonlinear behaviors make us analyze physiological systems in the new viewpoint. And, some of these nonlinear dynamics can be represented by chaotic behaviors, which is studied by several methods-correlation dimension, return map, power spectrum analysis, etc. This study is on the analysis of nonlinear characteristics of ECG. After data have been acquired from 20 children (10-13 years old), and 30 students (20-24 years old). We have calculated parameters HR, PR, VAT, TD, TRD, TPD from data, and estimated correlation dimension, return map, power spectrum, time series. Results show the nonlinear and chaotic characteristics of ECG.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.4
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• pp.283-288
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• 2015

In this paper, we propose a dynamic mathematical model of love involving various external forces, in order to analyze the chaotic phenomena in a love model based on Romeo and Juliet. In addition, we investigate the nonlinear phenomena in a love model with external forces using time series and phase portraits. In order to describe nonlinear phenomena precisely using time series and phase portraits, we vary the type of external force, using models such as a sine wave, chopping wave, and square wave. We also apply various different parameters in the Romeo and Juliet model to acquire chaotic dynamics.

• Steel and Composite Structures
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• v.6 no.2
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• pp.87-101
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• 2006

The deformation and dynamic behavior mechanism of submerged shell-like lattice structures with membranes are in principle of a non-conservative nature as circulatory system under hydrostatic pressure and disturbance forces of various types, existing in a marine environment. This paper deals with a characteristic analysis on quasi-periodic and chaotic behavior of a circular arch under follower forces with small disturbances. The stability region chart of the disturbed equilibrium in an excitation field was calculated numerically. Then, the periodic and chaotic behaviors of a circular arch were investigated by executing the time histories of motion, power spectrum, phase plane portraits and the Poincare section. According to the results of these studies, the state of a dynamic aspect scenario of a circular arch could be shifted from one of quasi-oscillatory motion to one of chaotic motion. Moreover, the correlation dimension of fractal dynamics was calculated corresponding to stochastic behaviors of a circular arch. This research indicates the possibility of making use of the correlation dimension as a stability index.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.651-653
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• 1998

This thesis propose the prefilter type control strategies using modified chaotic neural networks #or the trajectory control of robotic manipulator. Since the structure of chaotic neural networks and neurons, chaotic neural networks can show the robust characteristics for controlling highly nonlinear dynamics like robotic manipulators. For its application, the trajectory controller of the three-axis PUMA robot is designed by CNN. The CNN controller acts as the compensator of the PD controller. Simulation results show that learning error decrease drastically via on- line learning and the performance is excellent. The CNN controller have much better controllability and shorter calculation time compared to the RNN controller. Another advantage of the proposed controller could be attached to conventional robot controller without hardware changes.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.4
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• pp.184-191
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• 2001

This paper proposes a prefilter type velocity compensating control system using modified chaotic neural networks for the trajectory control of robotic manipulator. Since the structure of modified chaotic neural networks(MCNN) and neurons have highly nonlinear dynamic characteristics, MCNN can show the robust characteristics for controlling highly nonlinear dynamics like robotic manipulators. For its application, the trajectory controller of the three-axis robot manipulator is designed by MCNN. The MCNN controller acts as the compensator of the PD controller. Simulation results show that learning error decrease drastically via on-line learning and the performance is excellent. The MCNN controller showed much better control performance and shorter calculation time compared to the RNN controller, Another advantage of the proposed controller could by attached to conventional robot controller without hardware changes.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.2
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• pp.124-129
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• 2002

This paper demonstrates that the largest Lyapunov exponent λ of recurrent neural networks can be controlled efficiently by a stochastic gradient method. An essential core of the proposed method is a novel stochastic approximate formulation of the Lyapunov exponent λ as a function of the network parameters such as connection weights and thresholds of neural activation functions. By a gradient method, a direct calculation to minimize a square error (λ - λ$\^$obj/)$^2$, where λ$\^$obj/ is a desired exponent value, needs gradients collection through time which are given by a recursive calculation from past to present values. The collection is computationally expensive and causes unstable control of the exponent for networks with chaotic dynamics because of chaotic instability. The stochastic formulation derived in this paper gives us an approximation of the gradients collection in a fashion without the recursive calculation. This approximation can realize not only a faster calculation of the gradient, but also stable control for chaotic dynamics. Due to the non-recursive calculation. without respect to the time evolutions, the running times of this approximation grow only about as N$^2$ compared to as N$\^$5/T that is of the direct calculation method. It is also shown by simulation studies that the approximation is a robust formulation for the network size and that proposed method can control the chaos dynamics in recurrent neural networks efficiently.