• Title, Summary, Keyword: Objective functions

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A Development of Inequality Constrained Algorithm and Applying to Power System Analysis (부등호 제약조건 처리 알고리즘 개발 및 전력계통 해석 적용)

  • Yang, Minuk;Kim, Kern-Joong
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.62 no.10
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    • pp.1349-1353
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    • 2013
  • The optimal analysis has objective functions, equality constraint functions and inequality functions. Objective functions may be used with inequality function, because occasionally variables are moved to non-analytic condition with calculating objective functions. But inequality constraint functions are very complicated problem in a optimal analysis. this paper suggest a method to solve inequality constraint functions.

Contour Plots of Objective Functions for Feed-Forward Neural Networks

  • Oh, Sang-Hoon
    • International Journal of Contents
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    • v.8 no.4
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    • pp.30-35
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    • 2012
  • Error surfaces provide us with very important information for training of feed-forward neural networks (FNNs). In this paper, we draw the contour plots of various error or objective functions for training of FNNs. Firstly, when applying FNNs to classifications, the weakness of mean-squared error is explained with the viewpoint of error contour plot. And the classification figure of merit, mean log-square error, cross-entropy error, and n-th order extension of cross-entropy error objective functions are considered for the contour plots. Also, the recently proposed target node method is explained with the viewpoint of contour plot. Based on the contour plots, we can explain characteristics of various error or objective functions when training of FNNs proceeds.

Comparison of Objective Functions for Feed-forward Neural Network Classifiers Using Receiver Operating Characteristics Graph

  • Oh, Sang-Hoon;Wakuya, Hiroshi
    • International Journal of Contents
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    • v.10 no.1
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    • pp.23-28
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    • 2014
  • When developing a classifier using various objective functions, it is important to compare the performances of the classifiers. Although there are statistical analyses of objective functions for classifiers, simulation results can provide us with direct comparison results and in this case, a comparison criterion is considerably critical. A Receiver Operating Characteristics (ROC) graph is a simulation technique for comparing classifiers and selecting a better one based on a performance. In this paper, we adopt the ROC graph to compare classifiers trained by mean-squared error, cross-entropy error, classification figure of merit, and the n-th order extension of cross-entropy error functions. After the training of feed-forward neural networks using the CEDAR database, the ROC graphs are plotted to help us identify which objective function is better.

Interactive Fuzzy Multiobjective Decision-Making with Imprecise Goals (모호한 목표를 가진 대화형 퍼지 다목적 의사결정)

  • ;;Hong, S. L.
    • Journal of the Korean Operations Research and Management Science Society
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    • v.17 no.3
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    • pp.67-78
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    • 1992
  • MODM (multiobjective decision-making) problem is very complex system for the analyst. The problem is more complex if the goals of each of the objective functions are expressed imprecisely. It requires suitable MODM method to deal with imprecisions. Therefore, we present a new interactive fuzzy decision making method for solving multiobjective nonlinear programming problems by assuming that the decision maker (DM) has imprecise goals that assume fuzzy linguistic variable for each of the objective functions. The imprecise goals of the DM are quantified by eliciting corresponding membership functions through the interactive with the DM out of six membership functions. After determining membership functions, in order to generate the compromise or satisficing solution which is .lambda.-pareto optimal, .lambda.-max problem is solved. The higher degree of membership is chosen to satisfy imprecise goals of all objective functions by combining the membership functions. Then, the values are the compromise or satisficing solution. On the basis of the proposed method, and interactive computer programming is written to implement man-machine interactive procedures. Our programming is a revised version of sequential unconstrained minimization technique. Finally, a numerical example illustrates various aspects of the results developed in this paper.

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Full waveform inversion by objective functions with power and integral (지수 및 적분을 포함한 목적함수에 의한 파형역산)

  • Ha, Wan-Soo;Pyun, Suk-Joon;Shin, Chang-Soo
    • 한국지구물리탐사학회:학술대회논문집
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    • pp.130-134
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    • 2007
  • Classical full waveform inversion for velocity estimation defines the objective function as the $l^2$ -norm of differences between the modeled and the observed wavefields. Although widely used, the results of this method have been less than satisfactory. A moderate improvement of this method is to define the objective function as the $l^2$ -norm of differences between the logarithms of the modeled and observed wavefields. In this paper we propose new objective functions of waveform inversion. They produce better results in sub-salt imaging than those of the classical and the logarithmic objective functions. One objective function defines the residual as the difference between $L^{th}$ power of the modeled wavefields and that of the observed wavefields. Another defines the residual as the difference between the integral of the $L^{th}$ power of the modeled wavefields and that of the observed wavefields. We apply these new objective functions to the synthetic SEG/EAGE salt model, and show that our new waveform inversion algorithms provide more accurate results than those of the classical and logarithmic waveform inversion methods.

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Optimum Tire Contour Design Using Systematic STOM and Neural Network

  • Cho, Jin-Rae;Jeong, Hyun-Sung;Yoo, Wan-Suk;Shin, Sung-Woo
    • Journal of Mechanical Science and Technology
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    • v.18 no.8
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    • pp.1327-1337
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    • 2004
  • An efficient multi-objective optimization method is presented making use of neural network and a systematic satisficing trade-off method (STOM), in order to simultaneously improve both maneuverability and durability of tire. Objective functions are defined as follows: the sidewall-carcass tension distribution for the former performance while the belt-edge strain energy density for the latter. A back-propagation neural network model approximates the objective functions to reduce the total CPU time required for the sensitivity analysis using finite difference scheme. The satisficing trade-off process between the objective functions showing the remarkably conflicting trends each other is systematically carried out according to our aspiration-level adjustment procedure. The optimization procedure presented is illustrated through the optimum design simulation of a representative automobile tire. The assessment of its numerical merit as well as the optimization results is also presented.

Multi-objective Optimization of Fuzzy System Using Membership Functions Defined by Normed Method (노음방법에 의해 정의된 소속함수를 사용한 퍼지계의 다목적 최적설계)

  • 이준배;이병채
    • Transactions of the Korean Society of Mechanical Engineers
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    • v.17 no.8
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    • pp.1898-1909
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    • 1993
  • In this paper, a convenient scheme for solving multi-objective optimization problems including fuzzy information in both objective functions and constraints is presented. At first, a multi-objective problem is converted into single objective problem based on the norm method, and a merbership function is constructed by selecting its type and providing the parameters defined by the norm method. Finally, this fuzzy programming problem is converted into an ordinary optimization problem which can be solved by usual nonlinear programming techniques. With this scheme, a designer can conveniently obtain pareto optimal solutions of a fuzzy system only by providing some parameters corresponding to the importance of the objectiv functions. Proposed scheme is simple and efficient in treating multi-objective fuzzy systems compared with and method by with membership function value is provided interactively. To show the validity of the scheme, a simple 3-bar truss example and optimal cutting problem are solved, and the results show that the scheme is very useful and easy to treat multi-objective fuzzy systems.

Comparison of Cost Function of IMRT Optimization with RTP Research Tool Box (RTB)

  • Ko, Young-Eun;Yi, Byong-Yong;Lee, Sang-Wook;Ahn, Seung-Do;Kim, Jong-Hoon;Park, Eun-Kyung
    • Proceedings of the Korean Society of Medical Physics Conference
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    • pp.65-67
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    • 2002
  • A PC based software, the RTP Research Tool Box (RTB), was developed for IMRT optimization research. The software was consisted of an image module, a beam registration module, a dose calculation module, a dose optimization module and a dose display module. The modules and the Graphical User Interface (GUI) were designed to easily amendable by negotiating the speed of performing tasks. Each module can be easily replaced to new functions for research purpose. IDL 5.5 (RSI, USA) language was used for this software. Five major modules enable one to perform the research on the dose calculation, on the dose optimization and on the objective function. The comparison of three cost functions, such as the uncomplicated tumor control probability (UTCP), the physical objective function and the pseudo-biological objective function, which was designed in this study, were performed with the RTB. The optimizations were compared to the simulated annealing and the gradient search optimization technique for all of the optimization objective functions. No significant differences were found among the objective functions with the dose gradient search technique. But the DVH analysis showed that the pseudo-biological objective function is superior to the physical objective function when with the simulated annealing for the optimization.

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Knowledge Discovery in Aerodynamic Design Space using Data Mining (데이터 마이닝을 통한 공력설계공간 지식습득)

  • Jeong, Sin-Gyu;;, 동북대학교
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.34 no.1
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    • pp.49-55
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    • 2006
  • Two data mining techniques, analysis of variance (ANOVA) and self-organizing map (SOM), are applied to knowledge discovery in aerodynamic design space. These methods make it possible to identify the effect of each design variable on the objective functions. Furthermore, ANOVA shows the effect of interaction between design variables on the objective function and SOM visualizes the trade-off among objective functions. Present methods are applied to the result of the supersonic wing design which includes 72 design variables and 4 objective functions.

Generalized evolutionary optimum design of fiber-reinforced tire belt structure

  • Cho, J.R.;Lee, J.H.;Kim, K.W.;Lee, S.B.
    • Steel and Composite Structures
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    • v.15 no.4
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    • pp.451-466
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    • 2013
  • This paper deals with the multi-objective optimization of tire reinforcement structures such as the tread belt and the carcass path. The multi-objective functions are defined in terms of the discrete-type design variables and approximated by artificial neutral network, and the sensitivity analyses of these functions are replaced with the iterative genetic evolution. The multi-objective optimization algorithm introduced in this paper is not only highly CPU-time-efficient but it can also be applicable to other multi-objective optimization problems in which the objective function, the design variables and the constraints are not continuous but discrete. Through the illustrative numerical experiments, the fiber-reinforced tire belt structure is optimally tailored. The proposed multi-objective optimization algorithm is not limited to the tire reinforcement structure, but it can be applicable to the generalized multi-objective structural optimization problems in various engineering applications.