• Title/Summary/Keyword: weighted least squares

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Multiobjective Space Search Optimization and Information Granulation in the Design of Fuzzy Radial Basis Function Neural Networks

  • Huang, Wei;Oh, Sung-Kwun;Zhang, Honghao
    • Journal of Electrical Engineering and Technology
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    • v.7 no.4
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    • pp.636-645
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    • 2012
  • This study introduces an information granular-based fuzzy radial basis function neural networks (FRBFNN) based on multiobjective optimization and weighted least square (WLS). An improved multiobjective space search algorithm (IMSSA) is proposed to optimize the FRBFNN. In the design of FRBFNN, the premise part of the rules is constructed with the aid of Fuzzy C-Means (FCM) clustering while the consequent part of the fuzzy rules is developed by using four types of polynomials, namely constant, linear, quadratic, and modified quadratic. Information granulation realized with C-Means clustering helps determine the initial values of the apex parameters of the membership function of the fuzzy neural network. To enhance the flexibility of neural network, we use the WLS learning to estimate the coefficients of the polynomials. In comparison with ordinary least square commonly used in the design of fuzzy radial basis function neural networks, WLS could come with a different type of the local model in each rule when dealing with the FRBFNN. Since the performance of the FRBFNN model is directly affected by some parameters such as e.g., the fuzzification coefficient used in the FCM, the number of rules and the orders of the polynomials present in the consequent parts of the rules, we carry out both structural as well as parametric optimization of the network. The proposed IMSSA that aims at the simultaneous minimization of complexity and the maximization of accuracy is exploited here to optimize the parameters of the model. Experimental results illustrate that the proposed neural network leads to better performance in comparison with some existing neurofuzzy models encountered in the literature.

Evaluation of Usefulness of IDEAL(Iterative decomposition of water and fat with echo asymmetry and least squares estimation) Technique in 3.0T Breast MRI (3.0T 자기공명영상을 이용한 유방 검사시 IDEAL기법의 유용성 평가)

  • Cho, Jae-Hwan
    • Journal of Digital Contents Society
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    • v.11 no.2
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    • pp.217-224
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    • 2010
  • The purpose of this study was to examine the usefulness of IDEAL technique in breast MRI by performing a quantitative comparative analysis in patients diagnosed with DCIS. On a 3.0T MR scanner, fat-suppressed T2-weighted images and T1-weighted images before and after contrast enhancement were obtained from 20 patients histologically diagnosed with ductal carcinoma in situ (DCIS). The findings from the quantitative image analysis are the following: 1) On T2-weighted images, SNR were not significantly different in the lesion area itself between the CHESS and IDEAL groups, while the IDEAL group showed higher SNR at the ductal area and fat area than the CHESS group. In addition, the CNR were higher for the IDEAL group in those regions. 2) On T1-weighted images before enhancement, SNR were not significantly different in the lesion area itself between the CHESS and IDEAL groups, while the IDEAL group showed higher SNR at the ductal area and fat area than the CHESS group. In addition, the CNR were higher for the IDEAL group in those regions. 3) On T1-weighted images after enhancement, SNR were not significantly different in the lesion area itself between the CHESS and IDEAL groups, while the IDEAL group showed higher SNR at the ductal area and fat area than the CHESS group.

Adaptive Control of End Milling Machine to Improve Machining Straightness (직선도 개선을 위한 엔드밀링머시인 의 적응제어)

  • 김종선;정성종;이종원
    • Transactions of the Korean Society of Mechanical Engineers
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    • v.9 no.5
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    • pp.590-597
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    • 1985
  • A recursive geometric adaptive control method to compensate for machining straightness error in the finished surface due to tool deflection and guideway error generated by end milling process is developed. The relationship between the tool deflection and the feedrate is modeled by a modified Taylor's tool life equation. Without a priori knowledge on the variations off cutting parameters, time varying parameters are then estimated by an exponentially windowed recursive least squares method with only post-process measurements of the straightness error. The location error is controlled by shifting the milling bed in the direction perpendicular to the finished surface and adding a certain amount of feedrate with respect to the tool deflection model before cutting. The waviness error is compensated by adjusting the feedrate during machining. Experimental results show that location error is controlled within a range of fixturing error of the bed on the guideway and that about 60% reduction in the waviness error can be achieved within a few steps of parameter adaption under wide operating ranges of cutting conditions even if the parameters do not converge to fixed values.

Efficient Noise Estimation for Speech Enhancement in Wavelet Packet Transform

  • Jung, Sung-Il;Yang, Sung-Il
    • The Journal of the Acoustical Society of Korea
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    • v.25 no.4E
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    • pp.154-158
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    • 2006
  • In this paper, we suggest a noise estimation method for speech enhancement in nonstationary noisy environments. The proposed method consists of the following two main processes. First, in order to receive fewer affect of variable signals, a best fitting regression line is used, which is obtained by applying a least squares method to coefficient magnitudes in a node with a uniform wavelet packet transform. Next, in order to update the noise estimation efficiently, a differential forgetting factor and a correlation coefficient per subband are used, where subband is employed for applying the weighted value according to the change of signals. In particular, this method has the ability to update the noise estimation by using the estimated noise at the previous frame only, without utilizing the statistical information of long past frames and explicit nonspeech frames by voice activity detector. In objective assessments, it was observed that the performance of the proposed method was better than that of the compared (minima controlled recursive averaging, weighted average) methods. Furthermore, the method showed a reliable result even at low SNR.

A Study on the Strain Measurement of Structure object by Electronic Process and Laser Interferometry (전자처리 및 Laser간섭에 의한 구조물의 Strain 측정에 관한 연구)

  • Jung, W.K.;Kim, K.S.;Yang, S.P.;Jung, H.C.;Kim, J.H.
    • Journal of the Korean Society for Precision Engineering
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    • v.12 no.10
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    • pp.40-49
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    • 1995
  • This paper presents the performance and problems in analysis method and testing system of Electronic Speckle Pattern Interferometry (ESPI) method, in measuring two - dimensional in-plane displacement. The anyalysis result of measurement by ESPE is quite comparable to that tof measurement by strain gauge method. This implies that the method of ESPE is a very effective tool in non-contact two-dimensional in-plane strain analysis. But there is a controversal point, measurment error. This error is discussed to be affected not by ESPE method itself, but by its analysis scheme of the interference fringe, where the first-order interpolation has been applied to the points of strain measured. In this case, it is turned out that the more errors would be occurred in the large interval of fringe. And so this paper describes a computer method for drawing when the height is available only for some arbitrary collection of points. The method is based on a distance-weighted, last- squares approximation technique with the weight varying with the distance of the data points.

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Analysis of the Effect of Urban Characteristics on the Number of COVID-19 Confirmed Patients (도시특성이 코로나19 확진자 수에 미치는 영향 분석)

  • Oh, Hoo;Bae, Min Ki
    • Journal of the Korean Society of Safety
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    • v.37 no.4
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    • pp.80-91
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    • 2022
  • The purpose of this study is to contribute to strengthening the response of local governments to the emergence of new infectious diseases by identifying the urban characteristics affecting their spread. To this end, the urban characteristics influencing the spread of infectious diseases were identified from previous studies. Moreover, the variations in the impact of urban characteristics that affected the number of confirmed COVID-19 patients was spatially analyzed using geographically weighted regression (GWR). The analysis indicated that the explanatory power of the GWR was approximately 12.4% higher than that of the ordinary least squares method. Moreover, the explanatory power of the model in the northern regions, such as Seoul, Gyeonggi, and Gangwon, was particularly high, indicating that the urban characteristics affecting the spread of COVID-19 vary by region. The results of this study can be used as a basis for suggesting the formulation of customized policies reflecting the characteristics of each local government rather than a uniform spread reduction policy.

Application of Common Random Numbers in Simulation Experiments Using Central Composite Design (중심합성계획 시뮬레이션 실험에서 공통난수의 활용)

  • Kwon, Chi-Myung
    • Journal of the Korea Society for Simulation
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    • v.23 no.3
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    • pp.11-17
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    • 2014
  • The central composite design (CCD) is often used to estimate the second-order linear model. This paper uses a correlation induction strategy of common random numbers (CRN) in simulation experiment and utilizes the induced correlations to obtain better estimates for the second-order linear model. This strategy assigns the CRN to all design points in the CCD. An appropriate selection of the axial points in CCD makes the weighted least squares (WLS) estimator be equivalent to ordinary least squares (OLS) estimator in estimating the linear model parameters of CCD. We analytically investigate the efficiency of this strategy in estimation of model parameters. Under certain conditions, this correlation induction strategy yields better results than independent random number strategy in estimating model parameters except intercept. The simulation experiment on a selected model supports such results. We expect a suggested random number assignment is useful in application of CCD in simulation experiments.

Tracking Control of Robotic Manipulators based on the All-Coefficient Adaptive Control Method

  • Lei Yong-Jun;Wu Hong-Xin
    • International Journal of Control, Automation, and Systems
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    • v.4 no.2
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    • pp.139-145
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    • 2006
  • A multi-variable Golden-Section adaptive controller is proposed for the tracking control of robotic manipulators with unknown dynamics. With a small sample time, the unknown dynamics of the robotic manipulator are denoted equivalently by a characteristic model of a 2-order multivariable time-varying difference equation. The coefficients of the characteristic model change slowly with time and some of their valuable characteristic relationships emerge. Based on the characteristic model, an adaptive algorithm with a simple form for the control of robotic manipulators is presented, which combines the multi-variable Golden-Section adaptive control law with the weighted least squares estimation method. Moreover, a compensation neural network law is incorporated into the designed controller to reduce the influence of the coefficients estimation error on the control performance. The results of the simulations indicate that the developed control scheme is effective in robotic manipulator control.

A study on robust regression estimators in heteroscedastic error models

  • Son, Nayeong;Kim, Mijeong
    • Journal of the Korean Data and Information Science Society
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    • v.28 no.5
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    • pp.1191-1204
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    • 2017
  • Weighted least squares (WLS) estimation is often easily used for the data with heteroscedastic errors because it is intuitive and computationally inexpensive. However, WLS estimator is less robust to a few outliers and sometimes it may be inefficient. In order to overcome robustness problems, Box-Cox transformation, Huber's M estimation, bisquare estimation, and Yohai's MM estimation have been proposed. Also, more efficient estimations than WLS have been suggested such as Bayesian methods (Cepeda and Achcar, 2009) and semiparametric methods (Kim and Ma, 2012) in heteroscedastic error models. Recently, Çelik (2015) proposed the weight methods applicable to the heteroscedasticity patterns including butterfly-distributed residuals and megaphone-shaped residuals. In this paper, we review heteroscedastic regression estimators related to robust or efficient estimation and describe their properties. Also, we analyze cost data of U.S. Electricity Producers in 1955 using the methods discussed in the paper.

Identification of Structural Parameters from Frequency Response Functions (주파수 응답함수를 이용한 구조 파라메터 예측)

  • Kim, Kyu-Sik;Kang, Yeon-June
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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    • 2007.11a
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    • pp.863-869
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    • 2007
  • An improved method based on a normal frequency response function (FRF) is proposed to identify structural parameters such as mass, stiffness and damping matrices directly from the FRFs of a linear mechanical system. The method for estimating structural parameters directly from the measured FRFs of a structure is presented. This paper demonstrates that the characteristic matrices are extracted more accurately by using a weighted equation and eliminating the matrix inverse operation. The method is verified for a four degree-of-freedom lumped parameter system and an eight degree-of-freedom finite element beam. Experimental verification is also performed for a free-free steel beam whose size and physical properties are the same as those of the finite element beam. The results show that the structural parameters, especially the damping matrix, can be estimated more accurately by the proposed method.

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