• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.400-402
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• 1997

This paper investigates the direct and adaptive control of mechatronic servo systems using modified chaotic neural networks (CNNs). For the performance evaluation of the proposed neural networks, we simulate the trajectory control of the X-Y table with direct control strategies. The CNN based controller demonstrates accurate tracking of the planned path and also shows superior performance on convergence and final error comparing with recurrent neural network(RNN) controller.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.530-532
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• 1998

In this paper, We Designed the torque compensatory controller for robot manipulator using modified chaotic neural networks with self feedback loop. The proposed torque compensatory controller compensate torque of the PD controller. In order to estimate the proposed controller, we implemented to the Cartesian space control of three-axis PUMA robot and compared the simulation results with recurrent neural networks(RNNs) controller. Simulation results show that the learning error drastically decrease at on-line learning. The proposed CNNs controller shows much better control performance and shorter processing time compared to the recurrent neural network controller in the robot trajectory control.

• 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.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.578-580
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• 1995

In this paper, a method for the daily maximum load forecasting which uses a chaotic time series in power system and artificial neural network. We find the characteristics of chaos in power load curve and then determine a optimal embedding dimension and delay time, For the load forecast of one day ahead daily maximum power, we use the time series load data obtained in previous year. By using of embedding dimension and delay time, we construct a strange attractor in pseudo phase plane and the artificial neural network model trained with the attractor font mentioned above. The one day ahead forecast errors are about 1.4% of absolute percentage average error.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.835-837
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• 1996

In this paper, a method for the daily maximum load forecasting which uses a chaotic time series in power system and artificial neural network(Back-propagation) is proposed. We find the characteristics of chaos in power load curve and then determine a optimal embedding dimension and delay time. For the load forecast of one day ahead daily maximum power, we use the time series load data obtained in previous year. By using of embedding dimension and delay time, we construct a strange attractor in pseudo phase plane and the artificial neural network model trained with the attractor mentioned above. The one day ahead forecast errors are about 1.4% for absolute percentage average error.

• Proceedings of the KOSOMBE Conference
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• v.1995 no.05
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• pp.265-269
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• 1995

In this study, we present a training method of radial basis function networks based on recursive modified Gram-Schmidt algorithm for single step prediction of chaotic time series. With single step predictions of Mackey-Glass time series and alpha-rhythm EEG which has chaotic characteristics, the radial basis function network trained by this method is compared with one trained by a classical non-recursive method and the radial basis function model proposed by X.D. He and A. Lapedes. The results show the effectiveness of the training method.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2002.06a
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• pp.367-371
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• 2002

I construct a chaotically driven contraction system having some analogy with the information transfer mechanism in the brain system especially from CA1 cell to CA3 cell known from the empirical result. And I consider the properties of the response system on a state space according to the external input into the drive neuron by observing the fractal hierarchical structure. Then I induce the relation between the information about state transition of the chaotic time series and the spatial information on a fractal attractor to confirm the possibility of encoding of time series data to spatial information.

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.1
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• pp.32-39
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• 2004

Biological neurons generally have chaotic characteristics for permanent or transient period. The effects of chaotic response of biological neuron have not yet been verified by using analytical methods. Even though the transient chaos of neuron could be beneficial to overcoming the local minimum problem, the permanent chaotic response gives adverse effect on optimization problems in general. To solve optimization problems, which are needed in almost all neural network applications such as pattern recognition, identification or prediction, and control, the neuron should have one stable fixed point. In this paper, the dynamic characteristics of the chaotic dynamic neuron and the condition that produces the chaotic response are analyzed, and the convergence conditions are presented.

• Fisheries and Aquatic Sciences
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• v.10 no.3
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• pp.159-169
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• 2007

The complex 2-dimensional movements of fish during an annual migration circuit were generated and simulated by a chaotic model of fish movement, which was expanded from a small-scale movement model. Fish migration was modeled as a neural network including stimuli, central decision-making, and output responses as variables. The input stimuli included physical stimuli (temperature, salinity, turbidity, flow), biotic factors (prey, predators, life cycle) and landmarks or navigational aids (sun, moon, weather), values of which were all normalized as ratios. By varying the amplitude and period coefficients of the klinokinesis index using chaotic equations, model results (i.e., spatial orientation patterns of migration through time) were represented as fish feeding, spawning, overwintering, and sheltering. Simulations using this model generated 2-dimesional annual movements of sea bream migration in the southern and western seas of the Korean Peninsula. This model of object-oriented and large-scale fish migration produced complicated and sensitive migratory movements by varying both the klinokinesis coefficients (e.g., the amplitude and period of the physiological month) and the angular variables within chaotic equations.

• Journal of Electrical Engineering and Technology
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• v.1 no.4
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• pp.546-549
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• 2006

In this paper the neuron operation based on Brillouin-active fiber in optical fiber is described. The inherent optical feedback by the backscattered stokes wave in optical fiber leads to instabilities in the form of optical chaos. Controlling of chaos induced transient instability in Brillouin-active fiber is implemented with Kerr nonlinearity having a non-instantaneous response in network systems. The controlling chaotic instabilities can lead to multistable periodic states; create optical logic 'on' or high level '1' or 'off', or low level '0'. It is theoretically possible to apply the multi-stability regimes as an optical memory device for encoding and decoding series and complex data transmission in optical systems.