• Title/Summary/Keyword: objective function

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A Method of Determining the Scale Parameter for Robust Supervised Multilayer Perceptrons

  • Park, Ro-Jin
    • Communications for Statistical Applications and Methods
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    • v.14 no.3
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    • pp.601-608
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    • 2007
  • Lee, et al. (1999) proposed a unique but universal robust objective function replacing the square objective function for the radial basis function network, and demonstrated some advantages. In this article, the robust objective function in Lee, et al. (1999) is adapted for a multilayer perceptron (MLP). The shape of the robust objective function is formed by the scale parameter. Another method of determining a proper value of that parameter is proposed.

POI Recommendation Method Based on Multi-Source Information Fusion Using Deep Learning in Location-Based Social Networks

  • Sun, Liqiang
    • Journal of Information Processing Systems
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    • v.17 no.2
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    • pp.352-368
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    • 2021
  • Sign-in point of interest (POI) are extremely sparse in location-based social networks, hindering recommendation systems from capturing users' deep-level preferences. To solve this problem, we propose a content-aware POI recommendation algorithm based on a convolutional neural network. First, using convolutional neural networks to process comment text information, we model location POI and user latent factors. Subsequently, the objective function is constructed by fusing users' geographical information and obtaining the emotional category information. In addition, the objective function comprises matrix decomposition and maximisation of the probability objective function. Finally, we solve the objective function efficiently. The prediction rate and F1 value on the Instagram-NewYork dataset are 78.32% and 76.37%, respectively, and those on the Instagram-Chicago dataset are 85.16% and 83.29%, respectively. Comparative experiments show that the proposed method can obtain a higher precision rate than several other newer recommended methods.

Development of Optimization Methodology for Laser Welding Process Automation Using Neural Network Model and Objective Function (레이저 용접공정의 자동화를 위한 신경망 모델과 목적함수를 이용한 최적화 기법 개발)

  • Park, Young-Whan
    • Transactions of the Korean Society of Machine Tool Engineers
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    • v.15 no.5
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    • pp.123-130
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    • 2006
  • In manufacturing, process automation and parameter optimization are required in order to improve productivity. Especially in welding process, productivity and weldablity should be considered to determine the process parameter. In this paper, optimization methodology was proposed to determine the welding conditions using the objective function in terms of productivity and weldablity. In order to conduct this, welding experiments were carried out. Tensile test was performed to evaluate the weldability. Neural network model to estimate tensile strength using the laser power, welding speed, and wire feed rate was developed. Objective function was defined using the normalized tensile strength which represented the weldablilty and welding speed and wire feed rate which represented the productivity. The optimal welding parameters which maximized the objective function were determined.

A novel PSO-based algorithm for structural damage detection using Bayesian multi-sample objective function

  • Chen, Ze-peng;Yu, Ling
    • Structural Engineering and Mechanics
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    • v.63 no.6
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    • pp.825-835
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    • 2017
  • Significant improvements to methodologies on structural damage detection (SDD) have emerged in recent years. However, many methods are related to inversion computation which is prone to be ill-posed or ill-conditioning, leading to low-computing efficiency or inaccurate results. To explore a more accurate solution with satisfactory efficiency, a PSO-INM algorithm, combining particle swarm optimization (PSO) algorithm and an improved Nelder-Mead method (INM), is proposed to solve multi-sample objective function defined based on Bayesian inference in this study. The PSO-based algorithm, as a heuristic algorithm, is reliable to explore solution to SDD problem converted into a constrained optimization problem in mathematics. And the multi-sample objective function provides a stable pattern under different level of noise. Advantages of multi-sample objective function and its superior over traditional objective function are studied. Numerical simulation results of a two-storey frame structure show that the proposed method is sensitive to multi-damage cases. For further confirming accuracy of the proposed method, the ASCE 4-storey benchmark frame structure subjected to single and multiple damage cases is employed. Different kinds of modal identification methods are utilized to extract structural modal data from noise-contaminating acceleration responses. The illustrated results show that the proposed method is efficient to exact locations and extents of induced damages in structures.

Development of Pareto strategy multi-objective function method for the optimum design of ship structures

  • Na, Seung-Soo;Karr, Dale G.
    • International Journal of Naval Architecture and Ocean Engineering
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    • v.8 no.6
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    • pp.602-614
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    • 2016
  • It is necessary to develop an efficient optimization technique to perform optimum designs which have given design spaces, discrete design values and several design goals. As optimization techniques, direct search method and stochastic search method are widely used in designing of ship structures. The merit of the direct search method is to search the optimum points rapidly by considering the search direction, step size and convergence limit. And the merit of the stochastic search method is to obtain the global optimum points well by spreading points randomly entire the design spaces. In this paper, Pareto Strategy (PS) multi-objective function method is developed by considering the search direction based on Pareto optimal points, the step size, the convergence limit and the random number generation. The success points between just before and current Pareto optimal points are considered. PS method can also apply to the single objective function problems, and can consider the discrete design variables such as plate thickness, longitudinal space, web height and web space. The optimum design results are compared with existing Random Search (RS) multi-objective function method and Evolutionary Strategy (ES) multi-objective function method by performing the optimum designs of double bottom structure and double hull tanker which have discrete design values. Its superiority and effectiveness are shown by comparing the optimum results with those of RS method and ES method.

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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Robust Optimization Using Supremum of the Objective Function for Nonlinear Programming Problems (비선형계획법에서 목적함수의 상한함수를 이용한 강건최적설계)

  • Lee, Se Jung;Park, Gyung Jin
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.38 no.5
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    • pp.535-543
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    • 2014
  • In the robust optimization field, the robustness of the objective function emphasizes an insensitive design. In general, the robustness of the objective function can be achieved by reducing the change of the objective function with respect to the variation of the design variables and parameters. However, in conventional methods, when an insensitive design is emphasized, the performance of the objective function can be deteriorated. Besides, if the numbers of the design variables are increased, the numerical cost is quite high in robust optimization for nonlinear programming problems. In this research, the robustness index for the objective function and a process of robust optimization are proposed. Moreover, a method using the supremum of linearized functions is also proposed to reduce the computational cost. Mathematical examples are solved for the verification of the proposed method and the results are compared with those from the conventional methods. The proposed approach improves the performance of the objective function and its efficiency.

An inverse approach based on uniform load surface for damage detection in structures

  • Mirzabeigy, Alborz;Madoliat, Reza
    • Smart Structures and Systems
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    • v.24 no.2
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    • pp.233-242
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    • 2019
  • In this paper, an inverse approach based on uniform load surface (ULS) is presented for structural damage localization and quantification. The ULS is excellent approximation for deformed configuration of a structure under distributed unit force applied on all degrees of freedom. The ULS make use of natural frequencies and mode shapes of structure and in mathematical point of view is a weighted average of mode shapes. An objective function presented to damage detection is discrepancy between the ULS of monitored structure and numerical model of structure. Solving this objective function to find minimum value yields damage's parameters detection. The teaching-learning based optimization algorithm has been employed to solve inverse problem. The efficiency of present damage detection method is demonstrated through three numerical examples. By comparison between proposed objective function and another objective function which make use of natural frequencies and mode shapes, it is revealed present objective function have faster convergence and is more sensitive to damage. The method has good robustness against measurement noise and could detect damage by using the first few mode shapes. The results indicate that the proposed method is reliable technique to damage detection in structures.

MULTIOBJECTIVE FRACTIONAL PROGRAMMING WITH A MODIFIED OBJECTIVE FUNCTION

  • Kim, Do-Sang
    • Communications of the Korean Mathematical Society
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    • v.20 no.4
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    • pp.837-847
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    • 2005
  • We consider multiobjective fractional programming problems with generalized invexity. An equivalent multiobjective programming problem is formulated by using a modification of the objective function due to Antczak. We give relations between a multiobjective fractional programming problem and an equivalent multiobjective fractional problem which has a modified objective function. And we present modified vector saddle point theorems.