• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.479-483
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• 1992

This paper defines the relationship between PI type fuzzy control system and conventional PI control system, and discusses the relationship of parameters and control action in fuzzy controller. The tuning algorithm that updates ouput variable scaling factor of fuzzy controller is proposed .The proposed sheme is applied to the simulations of 2 selected dynamical plants. The simulation results show that the controller is effective in controlling dynamical plants.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.254-257
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• 2002

In previous study about combinatorial optimization problem solver by using neural network, since Hopfield method, to converge into the optimum solution sooner and certainer is regarded as important. Namely, only static states are considered as the information. However, from a biological point of view, the dynamical system has lately attracted attention. Then we propose the "dynamical" combinatorial optimization problem solver using hysteresis neural network. In this article, the proposal system is evaluated by the N-Queen problem.

• Proceedings of the KIEE Conference
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• 1993.11a
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• pp.290-292
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• 1993

This paper investigates the identification of discrete time nonlinear system using neural networks with two hidden layers. A New learning method of both NNI and NNC is proposed. For control of the dynamical system we use two neural networks, one for identification and the other for control, and proposed NN control system is based on a framework of MRC. We define a closed loop error. In the proposed learning method, the identification error and the closed loop error are utilized to train the NNI, whareas the control error and the closed loop error are used to train the NNC, The simulation results show that the identification and control schemes suggested are practically feasible and effective.

• Smart Structures and Systems
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• v.5 no.1
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• pp.69-79
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• 2009

A non-clipped semi-active stochastic optimal control strategy for nonlinear structural systems with MR dampers is developed based on the stochastic averaging method and stochastic dynamical programming principle. A nonlinear stochastic control structure is first modeled as a semi-actively controlled, stochastically excited and dissipated Hamiltonian system. The control force of an MR damper is separated into passive and semi-active parts. The passive control force components, coupled in structural mode space, are incorporated in the drift coefficients by directly using the stochastic averaging method. Then the stochastic dynamical programming principle is applied to establish a dynamical programming equation, from which the semi-active optimal control law is determined and implementable by MR dampers without clipping in terms of the Bingham model. Under the condition on the control performance function given in section 3, the expressions of nonlinear and linear non-clipped semi-active optimal control force components are obtained as well as the non-clipped semi-active LQG control force, and thus the value function and semi-active nonlinear optimal control force are actually existent according to the developed strategy. An example of the controlled stochastic hysteretic column is given to illustrate the application and effectiveness of the developed semi-active optimal control strategy.

• Smart Structures and Systems
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• v.14 no.5
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• pp.743-763
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• 2014

The paper presents an investigation of the nonlinear dynamical system of an electrostatically actuated micro-cantilever by the incremental harmonic balance (IHB) method. An efficient approach is proposed to tackle the difficulty in expanding the nonlinear terms into truncated Fourier series. With the help of this approach, periodic and multi-periodic solutions are obtained by the IHB method. Numerical examples show that the IHB solutions, provided as many as harmonics are taken into account, are in excellent agreement with numerical results. In addition, an iterative algorithm is suggested to accurately determine period doubling bifurcation points. The route to chaos via period doublings starting from the period-1 or period-3 solution are analyzed according to the Floquet and the Feigenbaum theories.

• Journal of applied mathematics & informatics
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• v.29 no.3_4
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• pp.563-572
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• 2011

In this paper, the Lie symmetry analysis is performed on the three mixed second-order PDEs, which arise in fluid dynamics, nonlinear wave theory and plasma physics, etc. The symmetries and similarity reductions of the equations are obtained, and the exact solutions to the equations are investigated by the dynamical system and power series methods. Then, the exact solutions to the general types of PDEs are considered through a variable transformation. At last, the symmetry and integration method is employed for reducing the nonlinear ODEs.

• The Journal of Korea Robotics Society
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• v.2 no.2
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• pp.152-160
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• 2007

In this paper, two schemes are introduced for dynamic simulation of underwater robotic systems. One is principle of dynamical balance, which is an easy and powerful tool for formulating dynamic equations of composite systems such as underwater vehicle-manipulator system. In the dynamic modeling, this principle gives us the closed-form of dynamic equations on matrix Lie group. The other is geometric integration algorithm, called 4-th order explicit Munthe-Kaas method. By this method, the derived differential equations can be integrated preserving geometric structure. Adopting these two schemes, dynamic simulation of underwater vehicle- manipulator system can be conducted more easily and more reliably.

• Atmosphere
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• v.25 no.1
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• pp.155-167
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• 2015

A dynamical seasonal prediction system for boreal winter utilizing cryospheric information was developed. Using the Community Atmospheric Model, version3, (CAM3) as a modeling system, newly developed snow depth initialization method and sea ice concentration treatment were implemented to the seasonal prediction system. Daily snow depth analysis field was scaled in order to prevent climate drift problem before initializing model's snow fields and distributed to the model snow-depth layers. To maximize predictability gain from land surface, we applied one-month-long training procedure to the prediction system, which adjusts soil moisture and soil temperature to the imposed snow depth. The sea ice concentration over the Arctic region for prediction period was prescribed with an anomaly-persistent method that considers seasonality of sea ice. Ensemble hindcast experiments starting at 1st of November for the period 1999~2000 were performed and the predictability gain from the imposed cryospheric informations were tested. Large potential predictability gain from the snow information was obtained over large part of high-latitude and of mid-latitude land as a result of strengthened land-atmosphere interaction in the modeling system. Large-scale atmospheric circulation responses associated with the sea ice concentration anomalies were main contributor to the predictability gain.

• East Asian mathematical journal
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• v.31 no.5
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• pp.743-751
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• 2015

In this paper, we consider a discrete ratio-dependent predator-prey system with harvesting effect. In order to investigate dynamical behaviors of this system, first we find out all fixed points of the system and then classify their stabilities by using their Jacobian matrices and local stability method. Next, we display some numerical examples to substantiate theoretical results and finally, we show numerically, by means of a bifurcation diagram, that various dynamical behaviors including cycles, periodic doubling bifurcation and chaotic bands can be existed.

• International Journal of Contents
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• v.10 no.1
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• pp.47-53
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• 2014

The paper considers the challenges involved in measuring the similarities between time series, such as time shifts and the mixture of frequencies. To improve recognition accuracy, we investigate an improved linear dynamical system for discovering prominent features by exploiting the evolving dynamics and correlations in a time series, as the quality of unsupervised pattern recognition relies strongly on the extracted features. The proposed approach yields a set of compact extracted features that boosts the accuracy and reliability of clustering for time series data. Experimental evaluations are carried out on time series applications from the scientific, socio-economic, and business domains. The results show that our method exhibits improved clustering performance compared to conventional methods. In addition, the computation time of the proposed approach increases linearly with the length of the time series.