• 한국국방경영분석학회지
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• 제13권1호
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• pp.91-100
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• 1987

This paper deals with an algorithm for solving nonlinear programming problems with equality constraints. Nonlinear programming problems are transformed into a square sums of nonlinear functions by the Lagrangian multiplier method. And an iteration method minimizing this square sums is suggested and then an algorithm is proposed. Also theoretical basis of the algorithm is presented.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.565-567
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• 2004

In active noise control (ANC) systems, the convergence behavior of the conventional Filtered-X Least Mean Square (FXLMS) algorithm may be affected by nonlinear distortions in the secondary path (e.g., in the power amplifiers, loudspeakers, transducers, etc.), which may lead to degradation of the error-reduction performance of the ANC systems. In this paper, a stable FXLMS algorithm with fast convergence is proposed to compensate for undesirable nonlinear distortions in the secondary-path of ANC systems by employing the Volterra filtering approach. In particular, the proposed approach is based on the utilization of the conventional P-th order inverse approach to nonlinearity compensation in the secondary path of ANC systems. Finally, the simulation results showed that the proposed approach yields a better convergence behavior In the nonlinear ANC systems than the conventional FXLMS.

• 전기학회논문지
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• 제56권7호
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• pp.1315-1320
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• 2007

In this paper, we introduce a new fuzzy model called fuzzy combined polynomial neural networks, which are based on the representative fuzzy model named polynomial fuzzy model. In the design procedure of the proposed fuzzy model, the coefficients on consequent parts are estimated by using not general least square estimation algorithm that is a sort of global learning algorithm but weighted least square estimation algorithm, a sort of local learning algorithm. We are able to adopt various type of structures as the consequent part of fuzzy model when using a local learning algorithm. Among various structures, we select Polynomial Neural Networks which have nonlinear characteristic and the final result of which is a complex mathematical polynomial. The approximation ability of the proposed model can be improved using Polynomial Neural Networks as the consequent part.

• Wind and Structures
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• 제17권4호
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• pp.451-464
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• 2013

This study develops an extended inverse input estimation algorithm with intelligent adaptive fuzzy weighting to effectively estimate the unknown input wind load of nonlinear structural systems. This algorithm combines the extended Kalman filter and recursive least squares estimator with intelligent adaptive fuzzy weighting. This study investigated the unknown input wind load applied on a tower structural system. Nonlinear characteristics will exist in various structural systems. The nonlinear characteristics are particularly more obvious when applying larger input wind load. Numerical simulation cases involving different input wind load types are studied in this paper. The simulation results verify the nonlinear characteristics of the structural system. This algorithm is effective in estimating unknown input wind loads.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2008년도 하계학술대회 논문집 Vol.9
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• pp.525-526
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• 2008

For measuring nonlinear Vehicle Modeling based on 7-Depth Sensor, the neural networks are proposed m adaptive and in realtime. The structure of it is similar to recurrent neural networks; a delayed output as the input and a delayed error between the output of plant and neural networks as a bias input. In addition, we compute the desired value of hidden layer by an optimal method instead of transfering desired values by backpropagation and each weights are updated by RLS(Recursive Least Square). Consequently, this neural networks are not sensitive to initial weights and a learning rate, and have a faster convergence rate than conventional neural networks. This new neural networks is Error Estimated Neural Networks. We can estimate nonlinear models in realtime by the proposed networks and control nonlinear models.

• 전자공학회논문지
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• 제50권12호
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• pp.205-209
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• 2013

기존의 반향신호 억제기는 스피커와 마이크 사이의 선형 관계만을 고려하여, 마이크로 입력된 신호로 부터 반향신호를 억제한다. 하지만 실제적으로 스피커는 비선형성을 가지고 있으며, 이 때문에 기존의 반향신호 억제기는 비선형 반향신호 환경에서 그 성능이 저하된다. 본 논문에서는 스피커의 비선형성을 모델링하기에 적합한 주파수영역상의 Least square 방식의 Volterra filter를 적용한 비선형 반향신호 억제기를 제안하였다. 객관적 성능평가 방법인 Echo Return Loss Enhancement (ERLE)와 Speech Attenuation(SA)를 도입하여 제안된 알고리즘의 성능 검증에 사용하였다. 제안된 알고리즘이 기존의 반향신호 억제기보다 선형 및 비선형 반향 신호 환경에서 우수한 성능을 보이는 것을 확인하였다.

• 한국전자파학회논문지
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• 제19권9호
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• pp.1058-1064
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• 2008

본 논문에서는 RF(Radio Frequency) 중계기에서 그룹화 CMA(Constant Modulus Algorithm)와 LMS(Least Mean Square) 알고리즘을 이용하여 적응 필터를 적용시킨 새로운 혼합 간섭 제거기를 제안한다. 송신 안테나에서 수신안테나로 궤환되는 신호는 수신 시스템의 성능을 저하시킨다. 제안한 간섭 제거기는 그룹화 CMA 알고리즘 간섭 제거 기법을 적용시키기 때문에 기존 구조보다 나은 채널 적응 성능과 낮은 MSE(Mean Square Error)을 가진다. 이 구조는 기존 비선형 간섭 제거기에 비해 같은 MSE(Mean Square Error)에 대한 반복수와 하드웨어 복잡도를 줄여준다. 즉, 제안한 알고리즘은 LMS 알고리즘에 비해 평균 자승 에러가 적응 상수에 따라 2.5 dB 또는 4 dB 정도 낮은 값을 보였다. 또한, VSS(Variable Step Size)-LMS 알고리즘에 비해 수렴 속도가 빠르고, 비슷한 평균 자승 에러를 가진다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.366-366
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• 2000

In this paper, an adaptive neuro-fuzzy filter using the conditional fuzzy c-means(CFCM) methods is proposed. Usualy, the number of fuzzy rules exponentially increases by applying the grid partitioning of the input space, in conventional adaptive neuro-fuzzy inference system(ANFIS) approaches. In order to solve this problem, CFCM method is adopted to render the clusters which represent the given input and output data. Parameter identification is performed by hybrid learning using back-propagation algorithm and total least square(TLS) method. Finally, we applied the proposed method to the nonlinear channel equalization problem and obtained a better performance than previous works.

• 한국정보통신학회논문지
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• 제13권7호
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• pp.1321-1327
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• 2009

본 논문에서는 최근 방송 및 이동 통신 서비스가 광벙위하게 사용되고 서비스 영역의 용이한 확대로 인해 무선중계기에 대한 수요가 급격히 증가하고 있다. 그러나 무선중계기에서 발생되는 궤환 신호로 인한 발진현상이 발생 한다. 그룹화 LMS(Least Mean Square)와 CMA(Constant Modulus Algorithm) 알고리즘을 이용한 적응 필터를 적용시킨 새로운 혼합 간섭 제거기활 제안한다. 제안한 간섭 제거기는 그룹화 LMS 알고리즘 간섭 제거기법을 적용시키기 때문에 기존 구조보다 나은 채널 적응 성능과 낮은 MSE(Mean Square Error)을 가진다. 이 제안된 검출기는 수렴속도를 증가하면서 동시에 평균 자승 에러를 줄이기 위해 최소평균 자승 알고리즘에서 두 개의 적응화 상수를 이용한다. 이 구조는 기존 비선형 간섭제거기에 비해 같은 MSE(Mean Square Error)에 대한 반복수와 하드웨어 복잡도를 줄여준다.

• 한국군사과학기술학회지
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• 제17권2호
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• pp.182-188
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• 2014

Source localization is an important problem in the field of sonar and radar, etc. For the purpose of source localization, two or more spatially separated sensors are often used to measure the time difference of arrivals of a radiating source whose transmitted signal waveform is unknown. The NLS(Nonlinear Least Square) cost function with curve fitting method was proposed recently, which provide robust source localization performance by reducing estimation ambiguity. However, even this algorithm shows degraded performance in a multipath environment. To estimates source localization correctly, source localization algorithm that eliminate the effect of multipath signals is required. In this paper, PMP(Power Matching Procedure) is added to the algorithm, which provides improved source localization performance by properly cutting out the effect of multipath signals. Through simulation the performance of the proposed source localization algorithm is verified.