• Title/Summary/Keyword: Parameters Optimization

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Optimized Polynomial Neural Network Classifier Designed with the Aid of Space Search Simultaneous Tuning Strategy and Data Preprocessing Techniques

  • Huang, Wei;Oh, Sung-Kwun
    • Journal of Electrical Engineering and Technology
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    • v.12 no.2
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    • pp.911-917
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    • 2017

  • There are generally three folds when developing neural network classifiers. They are as follows: 1) discriminant function; 2) lots of parameters in the design of classifier; and 3) high dimensional training data. Along with this viewpoint, we propose space search optimized polynomial neural network classifier (PNNC) with the aid of data preprocessing technique and simultaneous tuning strategy, which is a balance optimization strategy used in the design of PNNC when running space search optimization. Unlike the conventional probabilistic neural network classifier, the proposed neural network classifier adopts two type of polynomials for developing discriminant functions. The overall optimization of PNNC is realized with the aid of so-called structure optimization and parameter optimization with the use of simultaneous tuning strategy. Space search optimization algorithm is considered as a optimize vehicle to help the implement both structure and parameter optimization in the construction of PNNC. Furthermore, principal component analysis and linear discriminate analysis are selected as the data preprocessing techniques for PNNC. Experimental results show that the proposed neural network classifier obtains better performance in comparison with some other well-known classifiers in terms of accuracy classification rate.

  • https://doi.org/10.5370/JEET.2017.12.2.911 인용 PDF KSCI

Shape Optimization of Cavitator for a Supercavitating Projectile Underwater (초공동(超空洞) 하의 수중 주행체 캐비데이터 형상최적설계)

  • Grandhli Ramana V.;Choi JooHo
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.28 no.10
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    • pp.1566-1573
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    • 2004

  • When a projectile travels at high speed underwater, supercavitating flow arises, in which a huge cavity is generated behind the projectile so that only the nose, i.e., the cavitator, of the projectile is wetted, while the rest of it should be surrounded by the cavity. In that case, the projectile can achieve very high speed due to the reduced drag. Furthermore if the nose of the body is shaped properly, the attendant pressure drag can be maintained at a very low value, so that the overall drag is also reduced dramatically. In this study, shape optimization technique is employed to determine the optimum cavitator shape for minimum drag, given certain operating conditions. Shape optimization technique is also used to solve the potential flow problem fur any given cavitator, which is a free boundary value problem having the cavity shape as unknown a priori. Analytical sensitivities are derived for various shape parameters in order to implement a gradient-based optimization algorithm. Simultaneous optimization technique is proposed for efficient cavitator shape optimization, in which the cavity and cavitator shape are determined in a single optimization routine.

  • https://doi.org/10.3795/KSME-A.2004.28.10.1566 인용 PDF KSCI

Ram Accelerator Optimization Using the Response Surface Method (반응면 기법을 이용한 램 가속기 최적설계에 관한 연구)

  • Jeon Yong-Hee;Jeon Kwon-Su;Lee Jae-Woo;Byun Yung-Hwan
    • 한국전산유체공학회:학술대회논문집
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    • 2000.05a
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    • pp.159-165
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    • 2000

  • In this paper, numerical study has been done for the improvement of the superdetonative ram accelerator performance and for the design optimization of the system. The objective function to optimize the premixture composition is the ram tube length required to accelerate projectile from initial velocity $V_o$ to target velocity $V_e$. The premixture is composed of $H_2,\;O_2,\;N_2$ and the mole numbers of these species are selected at design variables. RSM(Response Surface Methodology) which is widely used for the complex optimization problems is selected as the optimization technique. In particular, to improve the non-linearity of the response and to consider the accuracy and efficiency of the solution, design space stretching technique has been applied. Separate sub-optimization routine is introduced to determine the stretching position and clustering parameters which construct the optimum regression model. Two step optimization technique has been applied to obtain the optimal system. With the application of stretching technique, we can perform system optimization with a small number of experimental points, and construct precise regression model for highly non-linear domain. The error to compared with analysis result is only $0.01\%$ and it is demonstrated that present method can be applied more practical design optimization problems with many design variables.

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