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用Boltzmann机来实现神经网络的基础文章,系统的阐述了相关理论,很难得的好文章。
Date : 2008-10-13 Size : 125.48kb User : 闫君飞

用Boltzmann机来实现神经网络的基础文章,系统的阐述了相关理论,很难得的好文章。-Using Boltzmann machine neural network to realize the basis of article, systematic exposition of the relevant theory, it is difficult to get a good article.
Date : 2025-12-23 Size : 125kb User : 闫君飞

格子Boltzmann方法 格子Boltzmann方法是为了保留格子气自动机方法的优点,克服其缺点而发展起来的方法。 特别是1992年,钱跃弘、陈十一等的开创性工作(提出LBGK模型方法),使该方法广泛地应用到计算流体力学(单相流、多相流、多孔介质流、热对流、磁流体、反应-扩散等)。 -Lattice Boltzmann method of lattice Boltzmann method is to retain the lattice gas automata method has the advantage to overcome its shortcomings and has developed methods. Especially in 1992, Qian Yue-hong, Chen XI, such as the pioneering work (LBGK model proposed method), so that the method widely used in computational fluid dynamics (single-phase flow, multiphase flow, porous media flow, thermal convection, magnetic fluid, the reaction- the proliferation, etc.).
Date : 2025-12-23 Size : 734kb User : Wan Gang

Lattice Boltzmann Method
Date : 2025-12-23 Size : 520kb User : chang_gx

顶盖驱动流的格子Boltzmann模拟代-The top cover-driven to stream of the plaid Boltzmann simulation of on behalf of
Date : 2025-12-23 Size : 86kb User : 赵凯

latice boltzmann code fluid flow
Date : 2025-12-23 Size : 86kb User : nasreddine

二维多组分SC格子Boltzmann代码-2D MCMP lattice Boltzmann
Date : 2025-12-23 Size : 6kb User : hb8313

波尔兹曼机算法,一种流行的人工神经网络算法,可解决局部最小等问题-Boltzmann machine algorithm, a popular artificial neural network algorithm, solve the local minimum problem
Date : 2025-12-23 Size : 17kb User : 李奇

A document introduce LBE
Date : 2025-12-23 Size : 6.86mb User : wwwforest

通过建议一个人脸形状先验模型关注该问题,该模型基于受限Boltzmann Machines (RBM)及其变种构建。特别的,我们首先基于深度信任网络构建一个模型以获取接近正视角的表情变化的人脸形状变量。为了解决姿态变化问题,我们将正面人脸形状先验模型整合到一个3路(3-way)RBM模型,其可以获取正面人脸形状和非正面人脸形状间的关系。最后,我们建议一个方法,将人脸先验模型和人脸特征点的图像度量系统性地组合在一起。-we address this problem by proposing a face shape prior model that is constructed based on the Restricted Boltzmann Machines (RBM) and their variants. Specifically, we first construct a model based on Deep Belief Networks to capture the face shape variations due to varying facial expressions for near-frontal view. To handle pose variations, the frontal face shape prior model is incorporated into a 3-way RBM model that could capture the relationship between frontal face shapes and non-frontal face shapes. Finally, we introduce methods to systematically combine the face shape prior models with image measurements of facial feature points. Experiments on benchmark s show that with the proposed method, facial feature points can be tracked robustly and accurately even if faces have significant facial expressions and poses.
Date : 2025-12-23 Size : 1.31mb User : 郭继东

hinton数字识别的matlab源代码(受限玻尔兹曼机)-RBM(Restricted Boltzmann Manchine)
Date : 2025-12-23 Size : 19kb User : 牛晚安
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