• Title/Summary/Keyword: RBNN

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A Study on MRD Methods of A RAM-based Neural Net (RAM 기반 신경망의 MRD 기법에 관한 연구)

  • Lee, Dong-Hyung;Kim, Seong-Jin;Park, Sang-Moo;Lee, Soo-Dong;Ock, Cheol-Young
    • Journal of the Korea Society of Computer and Information
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    • v.14 no.9
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    • pp.11-19
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    • 2009
  • A RAM-based Neural Net(RBNN) which has multi-discriminators is more effective than RBNN with a discriminator. Experience Sensitive Cumulative Neural Network and 3-D Neuro System(3DNS) that accumulate the features point improved the performance of BNN, which were enabled to train additional and repeated patterns and extract a generalized pattern. In recognition process of Neural Net with multi-discriminator, the selection of class was decided by the value of MRD which calculates the accumulated sum of each class. But they had a saturation problem of its memory cells caused by learning volume increment. Therefore, the decision of MRD has a low performance because recognition rate is decreased by saturation. In this paper, we propose the method which improve the MRD ability. The method consists of the optimum MRD and the matching ratio prototype to generalized image, the cumulative filter ratio, the gap of prototype response MRD. We experimented the performance using NIST database of NIST without preprocessor, and compared this model with 3DNS. The proposed MRD method has more performance of recognition rate and more stable system for distortion of input pattern than 3DNS.

Optimization of Stacking Line and Blade Profile for Design of Axial Flow Fan Blade (중첩선과 단면형상을 고려한 축류 송풍기 날개의 최적설계)

  • Samad, Abdus;Lee, Ki-Sang;Jung, Sang-Ho;Kim, Kwang-Yong
    • 한국전산유체공학회:학술대회논문집
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    • 2008.03b
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    • pp.420-423
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    • 2008
  • This present work is to find optimum design of a NACA65 axial fan blade with weighted average surrogate model. The numerical analysis by Reynolds-average Navier-Stokes equations with shear stress turbulence(SST) is discretized by finite volume approximations and solved on hexahedral grids for flow analysis. The blade aerodynamic shape is modified by six design variables for the optimization. The blade profile as well as stacking line is modified to enhance blade total efficiency. Six design variables, airfoil maximum camber, maximum camber location, leading edge radius, trailing edge radius, lean angle at 50% span and lean angle at 100% span, are selected for blade profile to enhance the total efficiency. The PBA model which is basically weighted average of the basis surrogates is used to find the optimal design in the design space from the constructed response surface model for the objective function. By the optimization, the total efficiency is increased by 1.4%.

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Optimization of a Cooling Channel with Staggered Elliptical Dimples Using Neural Network Techniques (신경회로망기법을 사용한 타원형 딤플유로의 냉각성능 최적화)

  • Kim, Hyun-Min;Moon, Mi-Ae;Kim, Kwang-Yong
    • The KSFM Journal of Fluid Machinery
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    • v.13 no.6
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    • pp.42-50
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    • 2010
  • The present analysis deals with a numerical procedure for optimizing the shape of elliptical dimples in a cooling channel. The three-dimensional Reynolds-averaged Navier-Stokes (RANS) analysis is employed in conjunction with the SST model for predictions of the turbulent flow and the heat transfer. Three non-dimensional geometric design variables, such as the ellipse dimple diameter ratio, ratio of the dimple depth to the average diameter, and ratio of the distance between dimples to the pitch are considered in the optimization. Twenty-one experimental points within design space are selected by Latin Hypercube Sampling. Each objective function values at these points are evaluated by RANS analysis and producing optimal point using surrogate model. The linear combination of heat transfer coefficient and friction loss related terms with a weighting factor is defined as the objective function. The results show that the optimized elliptical dimple shape improves considerably the heat transfer performance than the circular dimple shape.