• 전기학회논문지
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• 제61권3호
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• pp.459-465
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• 2012

In this paper, we present a non-fragile guaranteed cost control design method for uncertain nonlinear systems with time varying delays in state and control input, even though the controller gain is perturbed. The uncertain nonlinear term in the systems is norm bounded and the linear matrix inequality(LMI) optimization method is employed as a stability analysis of the systems. We design a robust controller and show the asymptotical stability of uncertain time-varying systems based on Lyapunov method. Also, we guarantee a specific level of performance of the systems. The simulations are carried out to demonstrate the effectiveness of the proposed method.

• Journal of applied mathematics & informatics
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• 제22권1_2호
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• pp.49-65
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• 2006

A robust numerical method for a singularly perturbed second-order ordinary differential equation having two parameters with a discontinuous source term is presented in this article. Theoretical bounds are derived for the derivatives of the solution and its smooth and singular components. An appropriate piecewise uniform mesh is constructed, and classical upwind finite difference schemes are used on this mesh to obtain the discrete system of equations. Parameter-uniform error bounds for the numerical approximations are established. Numerical results are provided to illustrate the convergence of the numerical approximations.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.445-445
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• 2000

This paper proposes the MIMO indirect adaptive fuzzy controller to control the two-link manipulators. The input-output linearization technique, equivalent control input plus integral term, augmented error model and recursive least square adaptive law are used fer the controller. The linear type of fuzzifier-defuzzifier fuzzy logic system used for nonlinear function makes easy to farm the error model and able to follow the adaptive system approach. Such that control approach, the control system is not required joint speed and accerelation measurement and easy to implement and tune. The simulation results showed that the proposed controller has good control performance, stability, very small tracking error, decoupling, fast convergence, robust to parameter variation and load.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제23권4호
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• pp.283-300
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• 2019

In this paper, we propose a new semi-analytic approach based on the generalized Taylor series for solving nonlinear differential equations of fractional order. Assuming the solution is expanded as the generalized Taylor series, the coefficients of the series can be computed by solving the corresponding recursive relation of the coefficients which is generated by the given problem. This method is called the generalized differential transform method(GDTM). In several literatures the standard GDTM was applied in each sub-domain to obtain an accurate approximation. As noticed in [19], however, a direct application of the GDTM in each sub-domain loses a term of memory which causes an inaccurate approximation. In this work, we derive a new recursive relation of the coefficients that reflects an effect of memory. Several illustrative examples are demonstrated to show the effectiveness of the proposed method. It is shown that the proposed method is robust and accurate for solving nonlinear differential equations of fractional order.

• 한국디지털정책학회:학술대회논문집
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• 한국디지털정책학회 2003년도 창립학술대회
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• pp.11-24
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• 2003

It is far more expensive for companies to acquire new customers than it is to retain customers. As a result, companies are turning to Customer Relationship Management (CRM) in order to make decisions about managing the relationship and the profitability of those customer relationships. CRM is a strategy that integrates the concepts of Knowledge Management, Data Mining and Data Warehousing in order to support the organization's decision -making process to retain long-term and profitable relationships with its customers. This paper examines the design implications that CRM poses to data warehousing. We then present a robust data warehouse schema to support CRM analyses and decisions. For example, the proposed schema could be used to calculate customer profitability and to identify social networks of influence between customers. The paper also discusses future areas for research pertaining to CRM data warehousing and data mining.

• 한국경영과학회지
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• 제31권2호
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• pp.157-167
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• 2006

Estimating the effects of price increase on a company's sales is important task faced by managers. If consumer has prior information on price increase or expects it, there would be stockpiling and subsequent drops in sales. In addition, consumer can suppress demand in the short run. These factors make the sales dynamic and unstable. In this paper we develop a time series model to evaluate the sales patterns with stockpiling and short-term suppression of demand and also propose a forecasting procedure. For estimation, we use panel data and extend the model to Bayesian hierarchical structure. By borrowing strength across cross-sectional units, this estimation scheme gives more robust and reasonable result than one from the individual estimation. Furthermore, the proposed scheme yields improved predictive power in the forecasting of hold-out sample periods.

• 대한의용생체공학회:의공학회지
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• 제6권1호
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• pp.1-4
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• 1985

Background EEG signals can be represented as the sum of a conventional AR process and an innovation process. It is know that conventional estimation techniques, such as least square estimates (LSE) or Gauasian maximum likelihood estimates (MLE-G) are optimal when the innovation process satisfies the Gaussian or presumed distribution. When the data are contaminated by outliers, however, these assumptions are not met and the power spectrum estimated by conventional estimation techniques may be fatally biased. EEG signal may be affected by artifacts, which are outliers in the statistical term. So the robust filtering estimation technique is used against those artifacts and it performs well for the contaminated EEG signal.

• Journal of Advanced Marine Engineering and Technology
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• 제23권4호
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• pp.480-487
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• 1999

In this paper we propose a neural network precompensated PID(NNP PID) controller for load frequency control of 2-area power system. While proportional integral derivative(PID) controllers are used in power system they have many problems because of high nonlinearities of the power system So a neural network-based precompensation scheme is adopted into a conventional PID controller to obtain a robust control to the nonlinearities. The applied neural network precompen-sator uses an error back-propagation learning algorithm having error and change of error as inputand considers the changing component of forward term of weighting factor for reducing of learning time. Simulation results show that the proposed control technique is superior to a conventional PID controller and an optimal controller in dynamic responses about load disturbances. The pro-posed technique can be easily implemented by adding a neural network precompensator to an existing PID controller.

• 전자공학회논문지B
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• 제31B권8호
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• pp.211-218
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• 1994

In this paper we develope a hybrid learning rule to improve the robustness of multi-layer Perceptions. In most neural networks the activation of a neuron is deternined by a nonlinear transformation of the weighted sum of inputs to the neurons. Investigating the behaviour of activations of hidden layer neurons a new learning algorithm is developed for improved robustness for multi-layer Perceptrons. Unlike other methods which reduce the network complexity by putting restrictions on synaptic weights our method based on error-backpropagation increases the complexity of the underlying proplem by imposing it saturation requirement on hidden layer neurons. We also found that the additional gradient-descent term for the requirement corresponds to the Hebbian rule and our algorithm incorporates the Hebbian learning rule into the error back-propagation rule. Computer simulation demonstrates fast learning convergence as well as improved robustness for classification and hetero-association of patterns.

• 조명전기설비학회논문지
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• 제12권2호
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• pp.108-114
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• 1998

본 논문에서는 외란이나 시스템의 파라미터 변동 및 불확실성 등이 존재하는 설비시스템을 강인하고 정밀하게 제어할 수 있도록 하기 위해 학습제어 기법을 적용하는데 있어서, 그 수렴 성능을 향상시키고 외란에 대한 시스템의 강인성을 증대시키며 아울러 시스템을 더욱 안정화시키기 위한 방법으로서 궤환을 갖는 반복 학습제어 알고리즘을 제안하고, 이 알고리즘에 의해 시스템 출력이 목표 출력에 수렴한다는 것을 증명해 보이며 수렴하기 위한 조건들을 제시한다. 제안된 알고리즘은 일반적인 형태의 2차 선형 시변 시스템에 대해 적용한 시뮬레이션을 통해, 기존의 알고리즘들을 적용했을 때의 결과보다 수렴 성능과 외란에 대한 강인성이 훨씬 향상되었음을 확인할 수 있었다.