• 한국ITS학회 논문지
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• 제13권1호
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• pp.127-134
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• 2014

본 연구에서는 X-밴드 taper 슬롯구조 능동위상 배열 안테나를 구현하였으며, 구현한 안테나의 성능을 해석하고 측정을 통하여 분석하였다. 제작된 광대역 위상배열 안테나의 능동반사계수 및 능동복사패턴을 측정한 결과를 통하여 설계된 위상배열 안테나의 능동반사계수 및 능동복사패턴 결과와 일치함을 확인하였고, 안테나의 광대역 빔 특성을 검증하여 설계 목표에 부합하는 안테나 구조를 제안하였다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.83-86
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• 1998

In this paper, a fuzzy model based on the Polynomial Neural Network(PNN) structure is proposed to estimate the emission pattern for air pollutant in power plants. the new algorithm uses PNN algorithm based on Group Mehtod of Data Handling (GMDH) algorithm and fuzzy reasoning in order to identify the premise structure and parameter of fuzzy implications rules, and the least square method in order to identify the optimal consequence parameters. Both time series data for the gas furnace and data for the NOx emission process of gas turbine power plants are used for the purpose of evaluating the performance of the fuzzy model. The simulation results show that the proposed technique can produce the optimal fuzzy model with higher accuracy and feasibility than other works achieved previously.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.453-458
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• 1998

In this paper, a new approach to modeling of nonlinear systems using fuzzy theory is presented. So as to handle a variety of nonlinearity and reflect the degree of confidence in the informations about system, we combine multiple model method with hierarchical prioritized structure. The mountain clustering technique is used in partition of system, and TSK rule structure is adopted to form the fuzzy rules. Back propagation algorithm is used for learning parameters in the rules. Computer simulations are performed to verify the effectiveness of the proposed method. It is useful for the treatment fo the nonlinear system of which the quantitative math-approach is difficult.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.395-398
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• 1998

In this paper, a method for the improvement of learning speed and convergence rate was proposed applied it to physiological neural structure with the advantages of artificial neural networks and fuzzy theory to physiological neuron structure, To compare the proposed method with conventional the single layer perception algorithm, we applied these algorithms bit parity problem and pattern recognition containing noise. The simulation result indicated that our learning algorithm reduces the possibility of local minima more than the conventional single layer perception does. Furthermore we show that our learning algorithm guarantees the convergence.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 추계학술대회 학술발표 논문집
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• pp.259-262
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• 1997

In this paper, a fuzzy model based on the polynomial Neural Network(PNN) structure is proposed to estimate the emission pattern for air pollutant in power plants. The new algorithm uses PNN algorithm based on Group Method of Data Handling (GMDH) algorithm and fuzzy reasoning in order to identify the premise structure and parameter of fuzzy implications rules, and the least square method in order to identify the optimal consequence parameters. Both time series data for the gas furnace and data for the NOx emission process of gas turbine power plants are used for the purpose of evaluating the performance of the fuzzy model. The simulation results show that the proposed technique can produce the optimal fuzzy model with higher accuracy anhd feasibility than other works achieved previously.

• 한국ITS학회 논문지
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• 제4권2호
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• pp.67-72
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• 2005

본 연구에서는 비대칭 직렬 연결 결합 선로에 기반한 새로운 결합 선로 결합기 구조를 제안하였다. 제시된 구조는 종래의 단일 결합 선로 결합기에 비해 보다 작은 크기로 설계, 제작될 수 있는 장점을 가진다. 또한 주어진 구조로부터 추가로 얻을 수 있는 설계 자유도를 이용하여 결합기의 출력 위상 조절이나 복잡한 회로 환경에서 결합기 레이아웃을 좀더 다양하게 하는 등 다른 목적으로도 효과적으로 이용될 수 있다.

• 한국지능시스템학회논문지
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• 제3권3호
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• pp.56-67
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• 1993

A Class of fuzzy controller based on the variable structure system(VSS) technique in which different structures of controllers are fuzzily switched according to the switching rules in proppsed. The structure of proposed controllers was motivated by the characteristics of position type fuzzy controller and velocity type fuzzy controller ; the former generally gives good performance in transient perod and the latter are capable of reducing steady state error of response. To show the usefulness of the proposed controller, it is applied to several systems that is difficult to stabilize or difficult to get satisfactory responsed by conventional fuzzy controllers.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 추계학술대회 학술발표 논문집
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• pp.474-478
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• 1998

A new approach for the decision feedback equalizer(DFE) based on the back-propagation neural networks is described. We propose the method of optimal structure for back-propagation neural networks model. In order to construct an the optimal structure, we first prescribe the bounds of learning procedure, and the, we employ the method of incrementing the number of input neuron by utilizing the derivative of the error with respect to an hidden neuron weights. The structure is applied to the problem of adaptive equalization in the presence of inter symbol interference(ISI), additive white Gaussian noise. From the simulation results, it is observed that the performance of the propose neural networks based decision feedback equalizer outperforms the other two in terms of bit-error rate(BER) and attainable MSE level over a signal ratio and channel nonlinearities.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 추계학술대회 학술발표 논문집
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• pp.324-329
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• 1998

In this paper, neural networks predictive PID (NNPPID) control system is proposed to reduce the vibration of structure. NNPPID control system is made up predictor, controller, and self-tuner to yield the optimal parameters of controller. The neural networks predictor forecasts the future outputs based on present input and output of structure. The controller is PID type whose parameters are yielded by neural networks self tuning algorithm. Computer simulations show displacements of multi-story structures applied to NNPPID system about environmental load-wind forces and earthquakes.

• Ocean Systems Engineering
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• 제9권2호
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• pp.179-190
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• 2019

This paper deals with the problem of the global stabilization for a class of ocean structure systems. It is well known that, in general, the global asymptotic stability of the ocean structure subsystems does not imply the global asymptotic stability of the composite closed-loop system. The classical fuzzy inference methods cannot work to their full potential in such circumstances because given knowledge does not cover the entire problem domain. However, requirements of fuzzy systems may change over time and therefore, the use of a static rule base may affect the effectiveness of fuzzy rule interpolation due to the absence of the most concurrent (dynamic) rules. Designing a dynamic rule base yet needs additional information. In this paper, we demonstrate this proposed methodology is a flexible and general approach, with no theoretical restriction over the employment of any particular interpolation in performing interpolation nor in the computational mechanisms to implement fitness evaluation and rule promotion.