• 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 advanced smart convergence
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• 제10권2호
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• pp.175-186
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• 2021

We have studied the speed regulation of the permanent magnet synchronous motor (PMSM) servo system in this paper. To optimize the PMSM servo system's speed-control performance with disturbances, a non-linear speed-control technique using a back-propagation neural network (BP-NN) algorithm forthe controller design of the PMSM speed loop is introduced. To solve the slow convergence speed and easy to fall into the local minimum problem of BP-NN, we develope an improved BP-NN control algorithm by limiting the range of neural network outputs of the proportional coefficient Kp, integral coefficient Ki of the controller, and add adaptive gain factor β, that is the internal gain correction ratio. Compared with the conventional PI control method, our improved BP-NN control algorithm makes the settling time faster without static error, overshoot or oscillation. Simulation comparisons have been made for our improved BP-NN control method and the conventional PI control method to verify the proposed method's effectiveness.

• 조명전기설비학회논문지
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• 제23권1호
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• pp.41-48
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• 2009

본 논문은 DC 전동기에 공급되는 PV 시스템의 최대전력점추적에 대한 신경회로망 제어기를 제시한다. 다양한 일사량은 PV 시스템의 MPPT에 대하여 가장 중요한 요소이다. 일사량은 비선형적, 비주기적이고 복잡하다. 신경회로망은 복잡한 수학적 문제를 해결하는데 광범위하게 사용되고 있다. 제안한 태양광 발전시스템은 신경회로망 제어기, DC-DC 컨버터, DC전동기, 부하로 구성되어 있다. 신경회로망 알고리즘은 컨버터의 쵸핑비를 계산하고 DC-DC 컨버터에 적용된다. 신경회로망의 출력은 수학적 모델링에 의해 계산된 값과 비교하고 알고리즘의 타당성을 제시한다.

• 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.

• 전기학회논문지
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• 제57권5호
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• pp.852-857
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• 2008

A new adaptive neuro-control algorithm for a SISO strict-feedback nonlinear system is proposed. All the previous adaptive neural control algorithms for strict-feedback nonlinear systems are based on the backstepping scheme, which makes the control law and stability analysis very complicated. The main contribution of the proposed method is that it demonstrates that the state-feedback control of the strict-feedback system can be viewed as the output-feedback control problem of the system in the normal form. As a result, the proposed control algorithm is considerably simpler than the previous ones based on backstepping. Depending heavily on the universal approximation property of the neural network (NN), only one NN is employed to approximate the lumped uncertain system nonlinearity. The Lyapunov stability of the NN weights and filtered tracking error is guaranteed in the semi-global sense.

• 융합신호처리학회논문지
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• 제3권2호
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• pp.30-36
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• 2002

본 논문에서는 선형적응필터를 사용하고 있는 기존의 적응잡음제거 기 의 단점을 보완하기 위해 신경망 적응필터를 이용한 비선형 적응잡음제거기를 다루고 있다. 제안된 적응잡음제거기는 광대역 시변 잡음신호를 사용하여 잡음제거 성능을 조사하였으며 상대평가를 위해 TDL (tapped-delay -line) 선형필터의 적응잡음제거기와 비교하였다. 실험결과에 의하면 적응잡음 제거기의 주입력에 포함된 잡음과 기준입력 사이에 비선형적인 상관관계가 존재하는 경우 신경망 적응잡음제거기는 평균자승오차값을 기준으로 선형잡음제거기보다 더 우수한 성능을 보여주었으며, 또한 리커런트 신경망 적응필터가 순방향 신경망 필터보다 성능이 우수하였다. 따라서 적응잡음제거기에서 광대역 시변잡음을 제거하는데 신경망 적응필터가 선형 적응필터보다 효과적임을 확인하였다.

• 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.

• 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.