• Journal of Intelligence and Information Systems
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• v.4 no.1
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• pp.133-147
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• 1998

Recently, many researchers have been involved in finding deterministic equations which can accurately predict future event, based on chaotic theory, or fractal theory. The theory says that some events which seem very random but internally deterministic can be accurately predicted by fractal equations. In contrast to the conventional methods, such as AR model, MA, model, or ARIMA model, the fractal equation attempts to discover a deterministic order inherent in time series data set. In discovering deterministic order, researchers have found that neural networks are much more effective than the conventional statistical models. Even though prediction accuracy of the network can be different depending on the topological structure and modification of the algorithms, many researchers asserted that the neural network systems outperforms other systems, because of non-linear behaviour of the network models, mechanisms of massive parallel processing, generalization capability based on adaptive learning. However, recent survey shows that prediction accuracy of the forecasting models can be determined by the model structure and data structures. In the experiments based on actual economic data sets, it was found that the prediction accuracy of the neural network model is similar to the performance level of the conventional forecasting model. Especially, for the data set which is deterministically chaotic, the AR model, a conventional statistical model, was not significantly different from the MLP model, a neural network model. This result shows that the forecasting model. This result shows that the forecasting model a, pp.opriate to a prediction task should be selected based on characteristics of the time series data set. Analysis of the characteristics of the data set was performed by fractal analysis, measurement of Hurst index, and measurement of Lyapunov exponents. As a conclusion, a significant difference was not found in forecasting future events for the time series data which is deterministically chaotic, between a conventional forecasting model and a typical neural network model.

• Nuclear Engineering and Technology
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• v.47 no.7
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• pp.838-848
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• 2015

One of the most important operations in nuclear power plants is load following, in which an imbalance of axial power distribution induces xenon oscillations. These oscillations must be maintained within acceptable limits otherwise the nuclear power plant could become unstable. Therefore, bounded xenon oscillation is considered to be a constraint for the load following operation. In this paper, the design of a sliding mode control (SMC), which is a robust nonlinear controller, is presented.SMCis ameansto control pressurized water nuclear reactor (PWR) power for the load following operation problem in a way that ensures xenon oscillations are kept bounded within acceptable limits. The proposed controller uses constant axial offset (AO) strategy to ensure xenon oscillations remain bounded. The constant AO is a robust state constraint for the load following problem. The reactor core is simulated based on the two-point nuclear reactor model with a three delayed neutron groups. The stability analysis is given by means of the Lyapunov approach, thus the control system is guaranteed to be stable within a large range. The employed method is easy to implement in practical applications and moreover, the SMC exhibits the desired dynamic properties during the entire output-tracking process independent of perturbations. Simulation results are presented to demonstrate the effectiveness of the proposed controller in terms of performance, robustness, and stability. Results show that the proposed controller for the load following operation is so effective that the xenon oscillations are kept bounded in the given region.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.102-107
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• 2021

This paper discusses the containment control problem for multi-agent systems with input saturations. The goal of the containment control is to obtain swarming behavior by driving follower agents into the convex hull which is spanned by multiple leader agents. This paper considers multiple leader agents moving at the same constant speed. Then, to solve the containment problem for moving leaders, we propose a PI-based distributed control algorithm. We next analyze the convergence of follower agents to the desired positions. Specifically, we apply the integral-type Lyapunov function to take into account the saturation nonlinearity. Then, based on Lasalle's Invariance Principle, we show that the asymptotic convergence of error states to zero for any positive constant gains. Finally, numerical examples with the static and moving leaders are provided to validate the theoretical results.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.4
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• pp.284-291
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• 2021

In this paper, we study the consensus problem for multi-agent systems with input saturations. The goal of consensus is to achieve a swarming behavior of multi-agent systems by reaching the agreement through information exchange. This paper considers agents modeled by first-order dynamics with input saturations. In order to guarantee the global convergence of the agents, it is assumed that the agents are stable. Moreover, considering the disturbances, we propose the PI based duplex control method to achieve the consensus. The proposed P controller and I controller are composed of different information network. Then, we investigate the conditions of the information networks and the control gains of P, I controllers to achieve the consensus applying the Lyapunov stability theorem and the Lasalle's Invariance Principle. Finally, we conduct the simulations to validate the theoretical results.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.2
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• pp.211-218
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• 2021

This paper addressed the problems of controlling the coupled pitch-roll motions in a marine vessel exposed to the regular waves in the longitudinal and transversal directions. Stabilization of the pitch and roll motions can be regarded as the essential task to ensure the safety of a ship's navigation. One of the important features in the pitch-roll motions is the resonance phenomena, which result in unexpected large responses in terms of pitch and roll modes in some specific conditions. Besides, owing to its inherent characteristics of coupled combination and nonlinearity of restoring terms, the vessel shows various dynamical behaviors according to the system parameters, especially in the pitch responses. Above all, it can be seen that suppression of pitch rate remains the most significant challenge to overcome for ship maneuvering safety studies. To secure the stable upright condition, a quasi-sliding mode control scheme is employed to reduce the undesirable pitch and roll responses as well as chattering elimination. The Lyapunov theory is adopted to guarantee the closed stability of the pitch-roll system. Numerical simulations demonstrate the effectiveness of the control scheme. Finally, the control goals of state convergences and chattering reduction are effectively realized through the proposed control synthesis.

• The Korean Journal of Financial Management
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• v.21 no.1
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• pp.149-181
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• 2004

In this study we perform empirical tests using KOSPI return to investigate the existence of nonlinear characteristics in the generating process of stock returns. There are three categories in empirical tests; the test of nonlinear dependence, nonlinear stochastic process and nonlinear deterministic chaos. According to the analysis of nonlinearity, stock returns are not normally distributed but leptokurtic, and appear to have nonlinear dependence. And it's decided that the nonlinear structure of stock returns can not be completely explained using nonlinear stochastic models of ARCH-type. Nonlinear deterministic chaos system is the feedback system, which the past incidents influence the present, and it is the fractal structure with self-similarity and has the sensitive dependence on initial conditions. To summarize the results of chaos analysis for KOSPI return, it is the persistent time series, which is not IID and has long memory, takes biased random walk, and is estimated to be fractal distribution. Also correlation dimension, as the approximation of fractal dimension, converged stably within 3 and 4, and maximum Lyapunov exponent has positive value. This suggests that chaotic attractor and the sensitive dependence on initial conditions exist in stock returns. These results fit into the characteristics of chaos system. Therefore it's decided that the generating process of stock returns has nonlinear deterministic structure and follow chaotic process.

• Journal for History of Mathematics
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• v.25 no.4
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• pp.53-67
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• 2012

Today, it is necessary to calculate orbits with high accuracy in space flight. The key words of Poincar$\acute{e}$ in celestial mechanics are periodic solutions, invariant integrals, asymptotic solutions, characteristic exponents and the non existence of new single-valued integrals. Poincar$\acute{e}$ define an invariant integral of the system as the form which maintains a constant value at all time $t$, where the integration is taken over the arc of a curve and $Y_i$ are some functions of $x$, and extend 2 dimension and 3 dimension. Eigenvalues are classified as the form of trajectories, as corresponding to nodes, foci, saddle points and center. In periodic solutions, the stability of periodic solutions is dependent on the properties of their characteristic exponents. Poincar$\acute{e}$ called bifurcation that is the possibility of existence of chaotic orbit in planetary motion. Existence of near exceptional trajectories as Hadamard's accounts, says that there are probabilistic orbits. In this context we study the eigenvalue problem in early 20th century in three body problem by analyzing the works of Darwin, Bruns, Gyld$\acute{e}$n, Sundman, Hill, Lyapunov, Birkhoff, Painlev$\acute{e}$ and Hadamard.