• Title/Summary/Keyword: Nonlinear Regression Model

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A study on position control of wheeled mobile robot using the inertial navigation system (관성항법시스템을 이용한 구륜 이동 로보트의 위치제어에 관한 연구)

  • 박붕렬;김기열;김원규;박종국
    • 제어로봇시스템학회:학술대회논문집
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    • 1996.10b
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    • pp.1144-1148
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    • 1996
  • This paper presents WMR modelling and path tracking algorithm using Inertial Navigation System. The error models of gyroscope and accelerometers in INS are derived by Gauss-Newton method which is nonlinear regression model. Then, to test availability of error model, we pursue the fitness diagnosis about probability characteristic for real data and estimated data. Performance of inertial sensor with error model and Kalman filter is pursued by comparing with one without them. The computer simulation shows that position error remarkably decrease when error compensation is applied.

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Efficient estimation and variable selection for partially linear single-index-coefficient regression models

  • Kim, Young-Ju
    • Communications for Statistical Applications and Methods
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    • v.26 no.1
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    • pp.69-78
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    • 2019
  • A structured model with both single-index and varying coefficients is a powerful tool in modeling high dimensional data. It has been widely used because the single-index can overcome the curse of dimensionality and varying coefficients can allow nonlinear interaction effects in the model. For high dimensional index vectors, variable selection becomes an important question in the model building process. In this paper, we propose an efficient estimation and a variable selection method based on a smoothing spline approach in a partially linear single-index-coefficient regression model. We also propose an efficient algorithm for simultaneously estimating the coefficient functions in a data-adaptive lower-dimensional approximation space and selecting significant variables in the index with the adaptive LASSO penalty. The empirical performance of the proposed method is illustrated with simulated and real data examples.

Improving Estimative Capability of Software Development Effort using Radial Basis Function Network (RBF 망 이용 소프트웨어 개발 노력 추정 성능향상)

  • Lee, Sang-Un;Park, Yeong-Mok;Park, Jae-Hong
    • The KIPS Transactions:PartD
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    • v.8D no.5
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    • pp.581-586
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    • 2001
  • An increasingly important facet of software development is the ability to estimated the associated coast and effort of development early in the development life cycle. In spite of the most generally sued procedures for estimation of the software development effort and cost were linear regression analysis. As a result of the software complexity and various development environments, the software effort and cost estimates that are grossly inaccurate. The application of nonlinear methods hold the greatest promise for achieving this objects. Therefore this paper presents an RBF (radial basis function) network model that is able to represent the nonlinear relation for software development effort, The research describes appropriate RBF network modeling in the context of a case study for 24 software development projects. Also, this paper compared the RBF network model with a regression analysis model. The RBF network model is the most accuracy of all.

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Optimal Design of Shock Absorber using High Speed Stability (고속 안정성을 고려한 쇽업소버 최적 설계)

  • 이광기;모종운;양욱진
    • Transactions of the Korean Society of Automotive Engineers
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    • v.6 no.4
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    • pp.1-8
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    • 1998
  • In order to solve the conflict problem between the ride comfort and the road holding, the optimal design of shock absorber that minimizes the r.m.s. of sprung mass vertical acceleration and pitch rate with the understeer characteristics constraints in the high speed stability is proposed. The design of experiments and the nonlinear optimization algorithm are used together to obtain the optimal design of shock absorber. The second order regression models of the input variables(front and rear damping coefficients) and the output variables (ride comfort index and road holding one) are obtained by the central composite design in the design of experiments. Then the optimal design of shock absorber can be systematically adjusted with applying the nonlinear optimization algorithm to the obtained second order regression model. The frequency response analysis of sprung mass acceleration and pitch rate shows the effectiveness of the proposed optimal design of shock absorber in the sprung mass resonance range with the understeer characteristics constraints.

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Strengthening of prestressed girder-deck system with partially debonding strand by the use of CFRP or steel plates: Analytical investigation

  • Haoran Ni;Riliang Li;Riyad S. Aboutaha
    • Computers and Concrete
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    • v.31 no.4
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    • pp.349-358
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    • 2023
  • This paper describes an in-depth analysis on flexural strength of a girder-deck system experiencing a strand debonding damage with various strengthening systems, based on finite element software ABAQUS. A detailed finite element analysis (FEA) model was developed and verified against the relevant experimental data performed by other researchers. The proposed analytical model showed a good agreement with experimental data. Based on the verified FE model, over a hundred girder-deck systems were investigated with the consideration of following variables: 1) debonding level, 2) span-to-depth ratio (L/d), 3) strengthening type, 4) strengthening material thickness. Based on the data above, a new detailed analytical model was developed and proposed for estimating residual flexural strength of the strand-debonding damaged girder-deck system with strengthening systems. It was demonstrated that both finite element model and analysis model could be used to predict flexural behaviors for debonding damaged prestressed girder-deck systems. Since the strands are debonding from surrounding concrete over a certain zone over the length of the beam, the increase of strain in strands can be linked with a ratio ψ, which is Lp/c. The analytical model was proposed and developed regarding the ratio ψ. By conducting procedure of calculating ψ, the ψ value varies from 9.3 to 70.1. Multiple nonlinear regression analysis was performed in Software IBM SPSS Statistics 27.0.1 to derive equation of ψ. ψ equation was curved to be an exponential function, and the independent variable (X) is a linear function in terms of three variables of debonding level (λ), span length (L), and amount of strengthening material (As). The coefficient of determinate (R2) for curve fitting in nonlinear regression analysis is 0.8768. The developed analytical model was compared to the ultimate capacities computed by FEA model.

OLED Power Driving Simulation Using Impedance Spectroscopy

  • Kong, Ung-Gul;Hyun, Seok-Hoon;Yoon, Chul-Oh
    • 한국정보디스플레이학회:학술대회논문집
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    • 2003.07a
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    • pp.32-35
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    • 2003
  • Nonlinear parameterization of OLED device from measurements of bias dependence of impedance spectra and parameter extraction using Levenberg-Marquardt complex nonlinear least square regression algorithm based on resistor-capacitor equivalent circuit model enables computer simulation of OLED power driving characteristics in forms of square-wave or sinusoidal output signal at arbitrary conditions. We introduce developed OLED power driving simulation software and discuss transient responses in voltage-or current-controlled operations as well as nonlinear characteristics of OLED, by presenting both the simulation and experimental results. This OLED simulation technique using impedance spectroscopy is extremely useful in predicting performance of the nonlinear device, especially in time-domain analysis of device operation.

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Comparing Fault Prediction Models Using Change Request Data for a Telecommunication System

  • Park, Young-Sik;Yoon, Byeong-Nam;Lim, Jae-Hak
    • ETRI Journal
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    • v.21 no.3
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    • pp.6-15
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    • 1999
  • Many studies in the software reliability have attempted to develop a model for predicting the faults of a software module because the application of good prediction models provides the optimal resource allocation during the development period. In this paper, we consider the change request data collected from the field test of the software module that incorporate a functional relation between the faults and some software metrics. To this end, we discuss the general aspect if regression method, the problem of multicollinearity and the measures of model evaluation. We consider four possible regression models including two stepwise regression models and two nonlinear models. Four developed models are evaluated with respect to the predictive quality.

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On a Novel Way of Processing Data that Uses Fuzzy Sets for Later Use in Rule-Based Regression and Pattern Classification

  • Mendel, Jerry M.
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.14 no.1
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    • pp.1-7
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    • 2014
  • This paper presents a novel method for simultaneously and automatically choosing the nonlinear structures of regressors or discriminant functions, as well as the number of terms to include in a rule-based regression model or pattern classifier. Variables are first partitioned into subsets each of which has a linguistic term (called a causal condition) associated with it; fuzzy sets are used to model the terms. Candidate interconnections (causal combinations) of either a term or its complement are formed, where the connecting word is AND which is modeled using the minimum operation. The data establishes which of the candidate causal combinations survive. A novel theoretical result leads to an exponential speedup in establishing this.

Planning of Streamflow Data Collection Network by Regionalized Regression Model (지역화회귀모형을 이용한 유량관측망의 계측)

  • 조국광;권순국
    • Water for future
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    • v.23 no.1
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    • pp.109-118
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    • 1990
  • In this study, the effectiveness of existing streamflow data collection networks in the Han and the Nakdong River Basin is evaluated for various gaging plans of 5, 10, 15 and 20years planning horizons by the nonlinear integer programming method, and also a technique for adjustment and planning of the existing network is provided for the purpose of increasing the efficiency of the network in terms of ecomony. The objective function is minimization of the average sampling mean square error of regional regression model with regression parameters estimated by generalized least squares method.

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Patch loading resistance prediction of plate girders with multiple longitudinal stiffeners using machine learning

  • Carlos Graciano;Ahmet Emin Kurtoglu;Balazs Kovesdi;Euro Casanova
    • Steel and Composite Structures
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    • v.49 no.4
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    • pp.419-430
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    • 2023
  • This paper is aimed at investigating the effect of multiple longitudinal stiffeners on the patch loading resistance of slender steel plate girders. Firstly, a numerical study is conducted through geometrically and materially nonlinear analysis with imperfections included (GMNIA), the model is validated with experimental results taken from the literature. The structural responses of girders with multiple longitudinal stiffeners are compared to the one of girders with a single longitudinal stiffener. Thereafter, a patch loading resistance model is developed through machine learning (ML) using symbolic regression (SR). An extensive numerical dataset covering a wide range of bridge girder geometries is employed to fit the resistance model using SR. Finally, the performance of the SR prediction model is evaluated by comparison of the resistances predicted using available formulae from the literature.