• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.6
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• pp.491-499
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• 2001

In this paper, the scheme of an efficient fuzzy rule generation and fuzzy system construction using GA(genetic algorithm) and FCM(fuzzy c-means) clustering algorithm is proposed for TSK(Takagi-Sugeno-Kang) type fuzzy system. In the structure identification, input data is transformed by PCA(Principal Component Analysis) to reduce the correlation among input data components. And then, a set fuzzy rules are generated for a given criterion by FCM clustering algorithm . In the parameter identification premise parameters are optimally searched by GA. On the other hand, the consequent parameters are estimated by RLSE(Recursive Least Square Estimate) to reduce the search space. From this one can systematically obtain the valid number of fuzzy rules which shows satisfying performance for the given problem. Finally, we applied the proposed method to the Box-Jenkins data and rice taste data modeling problems and obtained a better performance than previous works.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.4
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• pp.414-422
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• 2002

In this paper, we propose a new method of adaptive neuro-fuzzy control using CFCM(Conditional Fuzzy c-means) clustering and fuzzy equalization method to deal with adaptive control problem. First, in the off-line design, CFCM clustering performs structure identification of adaptive neuro-fuzzy control with the homogeneous properties of the given input and output data. The parameter identification are established by hybrid learning using back-propagation algorithm and RLSE(Recursive Least Square Estimate). In the on-line design, the premise and consequent parameters are tuned to RLSE with forgetting factor due to a characteristic of time variant. Finally, we applied the proposed method to the water temperature control system and obtained better results than previous works such as fuzzy control.

• Wind and Structures
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• v.11 no.1
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• pp.19-33
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• 2008

This research investigates the adaptive input estimation method applied to the multilayer shearing stress structure. This method is to estimate the values of wind load inputs by analyzing the active reaction of the system. The Kalman filter without the input term and the adaptive weighted recursive least square estimator are two main portions of this method. The innovation vector can be produced by the Kalman filter, and be applied to the adaptive weighted recursive least square estimator to estimate the wind load input over time. This combined method can effectively estimate the wind loads to the structure system to enhance the reliability of the system active performance analysis. The forms of the simulated inputs (loads) in this paper include the periodic sinusoidal wave, the decaying exponent, the random combination of the sinusoidal wave and the decaying exponent, etc. The active reaction computed plus the simulation error is regard as the simulated measurement and is applied to the input estimation algorithm to implement the numerical simulation of the inverse input estimation process. The availability and the precision of the input estimation method proposed in this research can be verified by comparing the actual value and the one obtained by numerical simulation.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.5
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• pp.833-842
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• 2017

In this study, the design methodology of radial basis function neural networks based on incremental K-means clustering is introduced for learning and processing the big data. If there is a lot of dataset to be trained, general clustering may not learn dataset due to the lack of memory capacity. However, the on-line processing of big data could be effectively realized through the parameters operation of recursive least square estimation as well as the sequential operation of incremental clustering algorithm. Radial basis function neural networks consist of condition part, conclusion part and aggregation part. In the condition part, incremental K-means clustering algorithms is used tweights of the conclusion part are given as linear function and parameters are calculated using recursive least squareo get the center points of data and find the fitness using gaussian function as the activation function. Connection s estimation. In the aggregation part, a final output is obtained by center of gravity method. Using machine learning data, performance index are shown and compared with other models. Also, the performance of the incremental K-means clustering based-RBFNNs is carried out by using PSO. This study demonstrates that the proposed model shows the superiority of algorithmic design from the viewpoint of on-line processing for big data.

• Journal of Drive and Control
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• v.13 no.4
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• pp.59-67
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• 2016

This paper proposes a model-prediction-based collision-avoidance algorithm for excavators for which the recursive-least-squares (RLS) estimation of the excavator's rotational inertia is used. To estimate the rotational inertia of the excavator, the RLS estimation with multiple forgetting and two updating rules for the nominal parameter and the forgetting factors was conducted based on the excavator-swing dynamics. The average value of the estimated rotational inertia that is for the minimizing effects of the estimation error was computed using the recursive-average method with forgetting. Based on the swing dynamics, the computed average of the rotational inertia, the damping coefficient for braking, and the excavator's braking angle were predicted, and the predicted braking angle was compared with the detected-object angle for a safety evaluation. The safety level defined in this study consists of the three levels safe, warning, and emergency braking. The analytical rotational-inertia-based performance evaluation of the designed estimation algorithm was conducted using a typical working scenario. The results of the safety evaluation show that the predictive safety-evaluation algorithm of the proposed model can evaluate the safety level of the excavator during its operation.

• Wind and Structures
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• v.17 no.4
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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.

• Journal of Auto-vehicle Safety Association
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• v.12 no.4
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• pp.13-22
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• 2020

This paper presents an adaptive feedback based actuator fault detection and tolerant control algorithms for longitudinal functional safety of autonomous driving. In order to ensure the functional safety of autonomous vehicles, fault detection and tolerant control algorithms are needed for sensors and actuators used for autonomous driving. In this study, adaptive feedback control algorithm to compute the longitudinal acceleration for autonomous driving has been developed based on relationship function using states. The relationship function has been designed using feedback gains and error states for adaptation rule design. The coefficients in the relationship function have been estimated using recursive least square with multiple forgetting factors. The MIT rule has been adopted to design the adaptation rule for feedback gains online. The stability analysis has been conducted based on Lyapunov direct method. The longitudinal acceleration computed by adaptive control algorithm has been compared to the actual acceleration for fault detection of actuators used for longitudinal autonomous driving.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.131-137
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• 2007

본 연구에서는 RLS(Rescursive Least Square) 추정 알고리즘을 이용하여 정전용량형 센서를 사용한 원격 RF 센서 시스템을 구현하고자 한다. IC 칩 형태의 원격 RF 센서 시스템이 가지는 구성의 복잡성 그리고 전력소모 문제를 해결하기 위해 보다 간단한 유도결합모델이 제안된다. 원격 RF 시스템은 페이저법을 이용하여 수학적으로 모델링되며, 모델기반의 RLS 알고리즘을 적용하기위해 시스템의 파라메타를 재배열한다. 오차 제곱합의 수렴특성을 가진 RLS 알고리즘을 이용하여 정전용량 파라메타를 추정한다. 실제 위상차를 측정하기 위해 Exclusive OR를 이용한 위상차 감지 장치를 제안한다. 센서로는 각종 환경 측정-습도, 압력 등-에 실제 활용되고 있는 정전용량형 센서를 채택한다. 잡음을 내포한 측정 데이터에 대한 추정 성능을 확인함으로써 그 유효성을 검증하고자 한다.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.760-762
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• 1999

본 논문에서는 기존의 적응필터인 LMS(Least Mean Square)와 RLS(Recursive Least Square)의 수렴속도의 향상과 안정성을 개선하기 위한 방안을 제안하였다. 제안된 알고리즘은 기존의 시간영역 LMS 알고리즘보다 상당히 빠른 수렴속도를 보일 수 있도록 설계하였다. RLS 알고리즘는 역행렬연산으로 인한 연산량이 많고 자기상관행렬이 positive definite 특성을 잃어버릴 경우 시스템이 수치적으로 불안정하게 되어 발산하는 단점이 있다. 이런한 단점을 보완하기 위해 제안된 알고리즘을 사용하였다. 기존의 알고리즘은 전력 정규화 과정에서 입력신호의 변환이 백색화가 완전히 이루어지지 않게 되어 자기상관행렬이 순수한 대각행렬이 되지 않는 단점을 지니고 있으나, 본 연구에서는 이러한 대각화 과정에서 좀더 많은 정보를 포함하도록 설계하였다. 아울러 제안된 알고리즘을 적응 등화기에 적용하여 수렴속도가 개선됨을 검증하였다.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.11
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• pp.1207-1218
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• 1990

The lack of persistent excitation (PE) can be the reason of freezing in the recursive least square estimators and the covariance windup in the exponential weighted least square estimators. We present a theoretical analysis of these phenomena and a simple method to check the exciting condition in real time. Using these results and under some conditions such as slowly time varying Plant and a tracking problem for set point, a robust self-tuning regulators without PE is proposed. In this algorithm, when PE is not satisfied, only plant gain is estimated, and then the system parameters are corrected by it. It is shown that the gain adaptive scheme makes the robustness to be improved against modeling error, off-set, and correlated noise etc, by the results of analysis and simulations.