• 품질경영학회지
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• 제49권2호
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• pp.201-211
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• 2021

Purpose: This paper proposes a density adaptive grid algorithm for the k-NN regression model to reduce the computation time for large datasets without significant prediction accuracy loss. Methods: The proposed method utilizes the concept of the grid with centroid to reduce the number of reference data points so that the required computation time is much reduced. Since the grid generation process in this paper is based on quantiles of original variables, the proposed method can fully reflect the density information of the original reference data set. Results: Using five real-life datasets, the proposed k-NN regression model is compared with the original k-NN regression model. The results show that the proposed density adaptive grid-based k-NN regression model is superior to the original k-NN regression in terms of data reduction ratio and time efficiency ratio, and provides a similar prediction error if the appropriate number of grids is selected. Conclusion: The proposed density adaptive grid algorithm for the k-NN regression model is a simple and effective model which can help avoid a large loss of prediction accuracy with faster execution speed and fewer memory requirements during the testing phase.

• International Journal of Control, Automation, and Systems
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• 제3권4호
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• pp.612-619
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• 2005

A robust adaptive speed sensorless induction motor direct torque control (DTC) using a neural network (NN) is presented in this paper. The inherent lumped uncertainties of the induction motor DTC system such as parametric uncertainty, external load disturbance and unmodeled dynamics are approximated by the NN. An additional robust control term is introduced to compensate for the reconstruction error. A control law and adaptive laws for the weights in the NN, as well as the bounding constant of the lumped uncertainties are established so that the whole closed-loop system is stable in the sense of Lyapunov. The effect of the speed estimation error is analyzed, and the stability proof of the control system is also proved. Experimental results as well as computer simulations are presented to show the validity and efficiency of the proposed system.

• 한국ITS학회 논문지
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• 제13권3호
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• pp.66-77
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• 2014

UTIS(Urban Traffic Information System)는 프로브차량을 활용하여 도시지역의 구간통행시간 정보를 직접 수집하는 방식으로 타 검지체계에 비해 상대적으로 정확한 링크 속도정보를 산출할 수 있다. 하지만, 현재 UTIS에서는 프로브차량(Probe Vehicle) 및 노변기지국(RSE)의 부족, 시스템 오류 등 다양한 요인에 의해 링크 속도정보의 수집이 누락되는 결측 구간이 발생되고 있다. 본 연구에서는 보다 정확한 여행시간 정보를 제공하기 위한 방안으로 k-NN 알고리즘을 기반으로 결측속도 정보를 효율적으로 보정할 수 있는 새로운 보정모형을 제안하였다. 제안 모형은 각 후보개체(이력 시계열 데이터)의 분포 특성에 따라 최근접이웃 개수를 탄력적으로 조정하는 적응형 k-NN 모형이다. 모형 평가 결과, 제안 모형이 결측정보를 효과적으로 보정 처리할 수 있는 동시에 ARIMA 등 타 모형에 비해 보정 오차를 크게 감소시킬 수 있는 것으로 분석되었다. 본 연구에서 제안된 결측 보정 모형은 UTIS 중앙교통정보센터에 직접 적용하여 교통정보 서비스 품질을 향상시키데 활용될 계획이다.

• 제어로봇시스템학회논문지
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• 제19권11호
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• pp.998-1003
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• 2013

This study proposes an adaptive control algorithm for lateral motion of a UGV (Unmanned Ground Vehicle) using an NN (Neural Networks). The lateral motion of the UGV can be corrupted with various uncertainties such as side slip. In order to compensate the performance degradation of the UGV under various uncertainties, an NN-based adaptive control is designed by utilizing a virtual control concept. Since both the drift and input gain terms are uncertain, the proposed method adapts the whole terms related to the difference between the nominal and real systems. To avoid a singularity problem with the adaptive control, the affine property of the UGV dynamic model is utilized and the overall closed-loop stability is analyzed rigorously. Finally, numerical simulations using Carsim are performed to validate the effectiveness of the proposed scheme.

• 제어로봇시스템학회논문지
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• 제20권12호
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• pp.1278-1283
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• 2014

This study proposes a NN (Neural Networks)-based adaptive control method for a 6X6 skid-type UGV (Unmanned Ground Vehicle) with 6 in-wheel motors. The UGV experiences lots of uncertainties and, thus, the control performance can degrade significantly without a compensation of the unknown terms. To improve the control performance of the UGV, the NN is utilized to design the adaptive controller. Then, the designed overall force and moment are optimally distributed into 6 traction forces with the assumption that six vertical forces of the UGV are known exactly, because the six traction forces are original source to be excited to the UGV to move. Finally, numerical simulations with the TruckSim model are performed to validate the effectiveness of the proposed approach.

• Journal of Electrical Engineering and Technology
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• 제10권1호
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• pp.408-421
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• 2015

Because the wheel of V-belt continuously variable transmission (CVT) system driven by permanent magnet synchronous motor (PMSM) has much unknown nonlinear and time-varying characteristics, the better control performance design for the linear control design is a time consuming job. In order to overcome difficulties for design of the linear controllers, a hybrid recurrent Chebyshev neural network (NN) control system is proposed to control for a PMSM servo-driven V-belt CVT system under the occurrence of the lumped nonlinear load disturbances. The hybrid recurrent Chebyshev NN control system consists of an inspector control, a recurrent Chebyshev NN control with adaptive law and a recouped control. Moreover, the online parameters tuning methodology of adaptive law in the recurrent Chebyshev NN can be derived according to the Lyapunov stability theorem and the gradient descent method. Furthermore, the optimal learning rate of the parameters based on discrete-type Lyapunov function is derived to achieve fast convergence. The recurrent Chebyshev NN with fast convergence has the online learning ability to respond to the system's nonlinear and time-varying behaviors. Finally, to show the effectiveness of the proposed control scheme, comparative studies are demonstrated by experimental results.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2000년도 추계학술대회 학술발표 논문집
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• pp.133-136
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• 2000

우리는 인터넷을 통한 사용자의 선호도(preference)를 분석하고 협력적 여과 기술을 학습하여 유머문서를 추천하는 MrHumor 시스템을 구축하였다. MrHumor에서는 사용자집합이 유머문서 집합에 대하여 보여준 등급매김값을 토대로 사용집합의 백터공간(vector space)를 설정하고 노이즈에 강하면서 효율적인 학습을 위해 선형 PCA를 이용하여 축소된 2차원 공간상에서 유머문서의 통계적 특성을 반영하여 적응형 k-NN으로 지엽성을 적적히 조절하여 새로운 문서에 대한 선호도를 추정하게 된다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 춘계학술대회 논문집 전기기기 및 에너지변환시스템부문
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• pp.221-223
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• 2008

This paper presents a maximum power point tracking(MPPT) of Photovoltaic system with chopping ratio of DC-DC converter considered load. A variation of solar irradiation is most important factor in the MPPT of PV system. That is nonlinear, aperiodic and complicated. The paper consists of solar radiation source, DC-DC converter, DC motor and load(cf, pump). NN algorithm apply to DC-DC converter through an adaptive control of neural network, calculates converter-chopping ratio using an adaptive control of NN. The results of an adaptive control of NN compared with the results of converter-chopping ratio which are calculated mathematical modeling and evaluate the proposed algorithm. The experimental data show that an adequacy of the algorithm was established through the compared data.

• 한국정밀공학회지
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• 제34권2호
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• pp.125-131
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• 2017

2-Axis Pan and Tilt Motion Platform, a complex multivariate non-linear system, may incur any disturbance, thus requiring system controller with robustness against various disturbances. In this study, we designed an adaptive backstepping compensated controller by estimating the disturbance and error using the Radial Basis Function Neural Network (RBF NN). In this process, Uniformly Ultimately Bounded (UUB) was demonstrated via Lyapunov and stability was confirmed. By generating progressive disturbance to the irregular frequency and amplitude changes, it was verified for various environmental disturbances. In addition, by setting the RBF NN input vector to the minimum, the estimated disturbance compensation process was analyzed. Only two input vectors facilitated compensatory function of RBF NN via estimating the modeling and control error values as well as irregular disturbance; the application of the process resulted in improved backstepping controller performance that was confirmed through simulation.

• 조명전기설비학회논문지
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• 제26권7호
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• pp.9-15
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• 2012

This paper is proposed the adaptive fuzzy neuro controller(AFNC) for high performance of induction motor drive. The design of this algorithm based on the AFNC that is implemented using fuzzy controller(FC) and neural network(NN). This controller uses fuzzy rule as training patterns of a NN. Also, this controller adjusts the weights between the neurons of NN to minimize the error between the command output and the actual output using the back-propagation method. The control performance of the AFNC is evaluated by analysis in various operating conditions. The results of analysis prove that the proposed control system has high performance and robustness to parameter variation, and steady-state accuracy and transient response.