• 제어로봇시스템학회:학술대회논문집
• /
• 1997.10a
• /
• pp.629-632
• /
• 1997

This paper presents the design method of controller which is combined Genetic Algorithms with the Generalized minimum variance self tuning controller. It is shown that the controllers adapts to changes in the system parameters with time delays and noises. The self tuning effect is achieved through the recursive least square algorithm at the parameter estimation stage and also through the Robbins-Monro algorithm at the stage of optimizing a polynomial parameters. The computer simulation results are presented to illustrate the procedure and to show the performance of the control system.

• Journal of Korean Institute of Industrial Engineers
• /
• v.20 no.4
• /
• pp.113-134
• /
• 1994

A radial basis hybrid neural network (RHNN) is presented for an on-line detection of machine condition change. Two-phase modeling by RHNN is designed for describing a machine condition process and for predicting future signal. A moving block procedure is also designed for detecting a process change. A fast on-line learning algorithm, the recursive least square estimation, is introduced. Experimental results showed the RHNN could be utilized efficiently for on-line machine condition monitoring.

• 제어로봇시스템학회:학술대회논문집
• /
• 1988.10a
• /
• pp.476-481
• /
• 1988

In this paper a pH process of a weak acid with a strong base is modeled into a bilinear form, and a self-tuning pH control algorithm which is robust against initial values of solution and disturbances is presented. The control algorithm employs the recursive least square method for the parameter estimation and the generalised minimum variance criterion as the objective function. The computer simulation shows that the tracking of desired pH values is obtained in satisfactory manner regardless of the initial values chosen for the process.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1994.10a
• /
• pp.371-376
• /
• 1994

This paper presents an on-line modeling method approach for the machine condition. the machine condition is continuously monitored with a sensor such as, a vibration, a current, an acoustic emission (AE) sensor. In this study, neural network modeling by radial basis function is designed for analysis a prediction error. An on-line learning algorithm is designed using the RLS(recursive least square) estimation and the existing clustering method of Kohonen neural network. Experimental results show that the proposed RBNN modeling is suitable for predicting simulated data.

• The Journal of the Acoustical Society of Korea
• /
• v.12 no.1E
• /
• pp.56-64
• /
• 1993

본 논문은 공간적으로 변하는 스펙트럼을 추정하는 새로운 적응 방법을 제안한다. 제안한 방법에서는 오래된 upstream의 데이터를 망각함으로서 신호의 nonstationarity를 고려해주는 시변망각인자의 개념을 recursive least square(RLS) 알고리즘에 도입하였으며, 관심이 있는 공간영역에서 탐사침을 천천히 움직여 얻은 하나의 데이터 군으로부터 downstream 스펙트럼을 추정하였다. 제시한 방법의 실현 가능성은 실제 실험(wind tunnel 이용)을 통해서 얻은 공간적으로 변하는 nonstatonary 신호의 스펙트럼을 추정하는 과정에서 입증되며 또한 기존의 방법들과 비교함으로서 그 우수성을 보인다.

• Proceedings of the KIEE Conference
• /
• 2003.07d
• /
• pp.2088-2090
• /
• 2003

A practical recursive weighted least square(WLS) solution is proposed to solve the passive ranging problem. Apart from the previous works based on the extended Kalman filter(EKF), to ensure the convergency at long-range, the proposed scheme makes use of line-of-sight(LOS) rate instead of bearing information. The influence of LOS rate measurement errors is investigated and it is asserted that the WLS estimates contain bias and scale factor errors. Together with simple compensation algorithm, the estimation errors of proposed filter can be reduced dramatically.

• Wind and Structures
• /
• v.17 no.4
• /
• pp.451-464
• /
• 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.

• Proceedings of the KOSOMBE Conference
• /
• v.1995 no.11
• /
• pp.265-268
• /
• 1995

Signal averaging technique to improve signal-to-noise ratio has widely been used in various fields, especially in electrophysiology. Estimation of the EP(evoked potential) signal using the conventional averaging method fails to correctly reconstruct the original signal under EEG(electroencephalogram) noise especial]y when the latency times of the evoked potential are not identical. Therefore, a technique based on the bispectrum averaging was proposed for recovering signal waveform from a set o noisy signals with variable signal dalay. In this paper an improved bispectrum estimation technique of the RP signal is proposed using a confidence thresholding of the EP signal in frequency domain in which energy distribution of the EP signal is usually not uniform. The suggested technique is coupled with the conventional bispectrum estimation technique such as least square method and recursive method. Some results with simulated data and real EP signal are shown.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2004.10a
• /
• pp.515-520
• /
• 2004

In this paper, a passive telemetry RF capacitive humidity sensor system using a RLSE(Recursive Least Square Estimation) technique is proposed. To overcome these trouble problems such as a power limitation and a estimation complexity that the general passive telemetry sensor system including It chip has, the principle of inductive coupling was applied to the modeling of a passive telemetry RF capacitive humidity sensor system and its capacitance was estimatedd by the RLSE algorithm. Specially, by introducing the optimal forgetting factor, we showed that the accuracy of its estimation was improved even in the time varying system and also the convergence time was reduced.

• Journal of Power Electronics
• /
• v.16 no.5
• /
• pp.1752-1762
• /
• 2016

To achieve higher reliability in the modular multilevel converters (MMC) for HVDC transmission systems, the internal condition of the DC capacitors of the submodules (SM) needs to be monitored regularly. For an online estimation of the SM capacitance, a controlled AC current with double the fundamental frequency is injected into the circulating current loop of the MMC, which results in current and voltage ripples in the SM capacitors. The capacitor currents are calculated from the arm currents and their switching states. By processing these AC voltage and current components with digital filters, their capacitances are estimated by a recursive least square (RLS) algorithm. The validity of the proposed scheme has been verified by simulation results for a 300-MW, 300-kV HVDC system. In addition, its feasibility has been verified by experimental results obtained with a reduced-scale prototype. It has been shown that the estimation errors for both the simulation and experimental tests are 1.32% at maximum.