• 전자공학회논문지CI
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• 제40권1호
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• pp.10-18
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• 2003

본 논문에서는 ADALINE의 학습을 위한 알고리즘을 제안하였다. 제안한 알고리즘은 직접 퍼지 논리 시스템을 이용하여 ADALINE의 연결강도를 조정하는 방식으로 퍼지 논리 시스템의 입력은 오차와 오차의 변화분이고, 출력은 연결강도 변화분이며, 각각의 연결강도는 스케일링 팩터만 다르게 하여 사용하였다. 제안한 알고리즘의 유용성을 확인하기 위하여 노이즈 제거와 인버티드 펜들럼 제어에 대하여 시뮬레이션과 실험을 수행하였다. Widrow-Hoff의 델타 규칙과 비교하였을 때 제안한 방식은 학습율을 선택할 필요도 없고, 성능이 우수함을 확인하였다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2001년도 하계종합학술대회 논문집(3)
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• pp.189-192
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• 2001

In this paper, we propose a ADALINE controller using fuzzy-backpropagation algorithm to adjust weight. In the proposed ADALINE controller, using fuzzy algorithm for traning neural network, controller make use of ADALINE due to simple and computing efficiency. This controller includes adaptive learning rate to accelerate teaming. It applies to servo-motor as an controlled process. And then it take a simulation for the position control, so the verify the usefulness of the proposed ADALINE controller.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2001년도 춘계종합학술대회
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• pp.684-687
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• 2001

본 논문에서는 퍼지-역전파 알고리즘을 이용하여 연결강도를 조정하고 ADALINE(Adaptive Linear Neuron)을 제어기로 사용하는 새로운 제어방식을 제안하였다. 제안된 ADALINE 제 어기는 퍼지 알고리즘을 이용하여 학습하고, 구조가 간단하고 계산량이 작은 장점으로 적응제어나 실시간 제어에 적합한 제어방식이다. 제안된 제어방식의 유용성을 입증하기 위하여 서보 전동기를 대상으로 위치제어에 대하여 시뮬레이션 하였다.

• 산업경영시스템학회지
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• 제19권39호
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• pp.47-61
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• 1996

We propose a parallel neural network model in which patterns are clustered and patterns in a cluster are studied in a parallel neural network. The learning algorithm used in this paper is based on LVQ algorithm of Kohonen(1990) for clustering and ADALINE(Adaptive Linear Neuron) network of Widrow and Hoff(1990) for parallel learning. The proposed algorithm consists of two parts. First, N patterns to be learned are categorized into C clusters by LVQ clustering algorithm. Second, C patterns that was selected from each cluster of C are learned as input pattern of ADALINE(Adaptive Linear Neuron). Data used in this paper consists of 250 patterns of ASCII characters normalized into $8\times16$ and 1124. The proposed algorithm consists of two parts. First, N patterns to be learned are categorized into C clusters by LVQ clustering algorithm. Second, C patterns that was selected from each cluster of C are learned as input pattern of ADALINE(Adaptive Linear Neuron). Data used in this paper consists 250 patterns of ASCII characters normalized into $8\times16$ and 1124 samples acquired from signals generated from 9 car models that passed Inductive Loop Detector(ILD) at 10 points. In ASCII character experiment, 191(179) out of 250 patterns are recognized with 3%(5%) noise and with 1124 car model data. 807 car models were recognized showing 71.8% recognition ratio. This result is 10.2% improvement over backpropagation algorithm.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 V
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• pp.2807-2810
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• 2003

In this paper, we propose a fuzzy control method for improving the control performance by automatically tuning the scaling factor. The proposed method is that automatically tune the input scaling factor and the output scaling factor of fuzzy logic system through neural network. Used neural network is ADALINE (ADAptive Linear NEron) neural network with delayed input. ADALINE neural network has simple construct, superior learning capacity and small computation time. In order to verify the effectiveness of the proposed control method, we performed simulation. The results showed that the proposed control method improves considerably on the environment of the disturbance.

• Journal of Power Electronics
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• 제15권6호
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• pp.1619-1627
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• 2015

This paper presents a power system harmonic elimination using the mixed adaptive linear neural network and variable step-size leaky least mean square (ADALINE-VSSLLMS) control algorithm based active power filter (APF). The weight vector of ADALINE along with the variable step-size parameter and leakage coefficient of the VSSLLMS algorithm are automatically adjusted to eliminate harmonics from the distorted load current. For all iteration, the VSSLLMS algorithm selects a new rate of convergence for searching and runs the computations. The adopted shunt-hybrid APF (SHAPF) consists of an APF and a series of 7^th tuned passive filter connected to each phase. The performance of the proposed ADALINE-VSSLLMS control algorithm employed for SHAPF is analyzed through a simulation in a MATLAB/Simulink environment. Experimental results of a real-time prototype validate the efficacy of the proposed control algorithm.

• Journal of Electrical Engineering and Technology
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• 제11권6호
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• pp.1714-1728
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• 2016

This paper presents improved harmonics extraction based on Adaptive Linear Neuron (ADALINE) algorithm for single phase photovoltaic (PV) shunt active power filter (SAPF). The proposed algorithm, named later as Improved ADALINE, contributes to better performance by removing cosine factor and sum of element that are considered as unnecessary features inside the existing algorithm, known as Modified Widrow-Hoff (W-H) ADALINE. A new updating technique, named as Fundamental Active Current, is introduced to replace the role of the weight factor inside the previous updating technique. For evaluation and comparison purposes, both proposed and existing algorithms have been developed. The PV SAPF with both algorithms was simulated in MATLAB-Simulink respectively, with and without operation or connection of PV. For hardware implementation, laboratory prototype has been developed and the proposed algorithm was programmed in TMS320F28335 DSP board. Steady state operation and three critical dynamic operations, which involve change of nonlinear loads, off-on operation between PV and SAPF, and change of irradiances, were carried out for performance evaluation. From the results and analysis, the Improved ADALINE algorithm shows the best performances with low total harmonic distortion, fast response time and high source power reduction. It performs well in both steady state and dynamic operations as compared to the Modified W-H ADALINE algorithm.

• Journal of Power Electronics
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• 제9권3호
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• pp.386-395
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• 2009

In this paper, a new control algorithm for the dynamic voltage restorer (DVR) is proposed to regulate the load terminal voltage during various power quality problems that include sag, swell, harmonics and unbalance in the voltage at the point of common coupling (PCC). The proposed control strategy is an Adaline (Adaptive linear element) Artificial Neural Network (ANN) and is used to control a capacitor supported DVR for power quality improvement. A capacitor supported DVR does not need any active power during steady state because the voltage injected is in quadrature with the feeder current. The control of the DVR is implemented through derived reference load terminal voltages. The proposed control strategy is validated through extensive simulation studies using the MATLAB software with its Simulink and SimPower System (SPS) toolboxes. The DVR is found suitable to support its dc bus voltage through the control under various disturbances.

• 정보교육학회논문지
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• 제9권2호
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• pp.167-176
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• 2005

교육현장에서 발생하는 여러 가지 의사결정 사항들을 효과적으로 처리하는 것은 매우 중요한 이슈로 부각되고 있다. 예를 들어, 교육인적자원부 입장에서 전국 초등학교를 대상으로 교육정보화 분야에 투자를 기획할 때 정확하고 효율적인 의사결정 과정은 국가적 차원에서 매우 중요한 사항인 것이다. 현재 이상과 같은 일련의 과정을 시행하기 위하여 현장의 설문조사나 관련 전문가를 활용한 기획이 이루어지고 있는 실정이 다. 그러나, 정보 기술분야의 급격한 발전으로, 의학이나 경영분야 등에서는 다양한 의사결정지원 도구 활용을 통한 최상의 의사 결정 방법을 찾고 있다. 이 같은 이유로 본 논문에서는 신경망의 ADALINE 알고리즘을 활용하여 교육분야에서 적용 가능한 의사결정 시스템 모델을 설계하였으며 또한 시뮬레이션을 통하여 구현 모델의 검증을 수행하였다. 개발된 교육용 의사결정 시스템으로 교육현장에서 발생하는 다양한 의사결정 사항들을 효과적으로 처리할 수 있을 것이다.

• 제어로봇시스템학회논문지
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• 제11권5호
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• pp.429-434
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• 2005

In this paper, the maze navigation system using image recognition for autonomous mobile robot is proposed. The proposed maze navigation system searches the target by image recognition method based on ADALINE neural network. The infrared sensor system must travel all blocks to find target because it can recognize only one block information each time. But the proposed maze navigation system can reduce the number of traveling blocks because of the ability of sensing several blocks at once. Especially, due to the simplicity of the algorithm, the proposed method could be easily implemented to the system which has low capacity processor.