搜索资源列表
biot
- 基于BIOT理论计算弹性波在多孔双相介质中的传播-elastic wave propagation- biot model
bisq
- 弹性波在饱和多孔介质中传播的BISQ模型-BISQ model for elastic wave propagation in porous media
BPTS_NetWork
- 一种基于误差反向传播的BP学习算法的模糊神经网络模型,作为非线性映射的有效工具。-A method based on back propagation BP learning algorithm for fuzzy neural network model of nonlinear mapping, as an effective tool.
walfishikegami
- this programm can meassure loss in propagation. this name is walfish ikegegami model
OHcandra
- this programm can meassure loss in propagation. this name is okummura hatta model
Tamir
- Tamir模型的源代码,主要研究在树林信号传播时的衰减模型。-The Tamir model source code, mainly in the woods signal propagation attenuation model.
leaves-bass-algorithm
- 这是一个贝叶斯独立分量分析(ICA)算法的线性瞬时混合高斯噪声模型和添加剂。解决问题的是ML-II推论,即资源的整合在发现源后和噪声协方差矩阵和混合了最大化的边际似然。充分统计量的估计平均场或变分理论和线性响应修正或通过自适应平均场理论水龙头。平均场方程,解决了信仰传播法的或连续的迭代。-This is a bayesian independent component analysis (ICA) algorithm of instantaneous linear mixed gaussian
xiaochushengsu
- 被动声方位估计中消除有效声速影响的方法。基于声传播的平面波模型,采用向量分析方法导出了十字阵和正四面体 阵的测向公式,分析了两者在有效声速变化条件下不同的定位性能;结果表明,对正 四面体阵,采用合适的算式,可在方位估计中避开有效声速的影响。-Passive Acoustic Direction Estimation method to eliminate the effective speed of sound effect. Cross array and regular tetrah
rayleighnew
- 瑞利衰落信道,是一种无线电信号传播环境的统计模型。这种模型假设信号通过无线信道之后,其信号幅度是随机的,即“衰落”,并且其包络服从瑞利分布。这一信道模型能够描述由电离层和对流层反射的短波信道,以及建筑物密集的城市环境。-Rayleigh fading channels, the statistical model of a radio signal propagation environment. This model assumes that after the signal through
multiScale_KalmanFilter
- 用多尺度卡尔曼滤波法,对信号参数进行识别估计。高频信号和低频信号识别结合起来改进了算法识别的精确度和准确度。-It is an implementation of hierarchical (a.k.a. multi-scale) Kalman filter using belief propagation. The model parameters are estimated by expectation maximization (EM) algorithm. In this impleme
ABC
- 数学建模 病毒传播SIS模型研究中第二小题的m文件,参考自无标度网络的代码,原先做题时运行生成A、B各5000个节点,100次重复运算取平均,整整跑了24083秒,将近7小时- -,后来有空对代码简化,同样的计算,只需要365秒,才6分钟…有兴趣的可以研究下。-The second theme m file in mathematical modeling virus propagation SIS model, reference scale network code, originally
BP-neural-network-prediction-method
- BP(Back Propagation)网络是1986年由Rumelhart和McCelland为首的科学家小组提出,是一种按误差逆传播算法训练的多层前馈网络,是目前应用最广泛的神经网络模型之一。BP网络能学习和存贮大量的输入-输出模式映射关系,而无需事前揭示描述这种映射关系的数学方程。它的学习规则是使用最速下降法,通过反向传播来不断调整网络的权值和阈值,使网络的误差平方和最小。BP神经网络模型拓扑结构包括输入层(input)、隐层(hide layer)和输出层(output layer)。-
LMS--FPGA
- The simulink model of Back propagation Algorithm using an Adaptative Filter.
decoder_BP_EB
- 通过单边选择实现低复杂度的置信传播(BP)算法,并将此算法应用于检查模型中,可有效提高系统的性能,且具有较低的复杂度-Select achieved through unilateral low-complexity belief propagation (BP) algorithm, and the algorithm is applied to check the model can effectively improve system performance, and has lower
BP_network
- BP神经网络,用于实现简单的二次函数的模拟,其具体的训练算法是误差反传-BP neural network, which is trained by error back-propagation algorithm, this example shows a simple quadratic function model.
StandardBPalgorithmCode
- 该代码是基于标准BP算法训练样本数据的代码,压缩包里包含代码和样本数据。 BP(Back Propagation)神经网络,即信号的正向传播+误差的反向传播,该网络是应用最广泛的一种神经网络模型。BP网络的设计主要包括输入层,隐层,输出层及各层之间的权值、阈值及传输函数等几个方面。-The code is based on code standard BP algorithm training data, compression bag containing the code and sampl
An-Introduction-to-Factor-Graphs
- 关于因子图和消息传递算法领域的非常经典的论文,绝对值得大家仔细阅读-A large variety of algorithms in coding, signal processing, and artificial intelligence may be viewed as instances of the summary-product algorithm (or belief/probability propagation algorithm), which operates
self-taught-learning
- 自主学习把稀疏自编码器和分类器实现结合。先通过稀疏自编码对无标签的5-9的手写体进行训练得到最优参数,然后通过前向传播,得到训练集和测试集的特征,通过0-4有标签训练集训练出softmax模型,然后输入测试集到分类模型实现分类。-Independent Learning the encoder and the sparse classifiers achieve the combination. First through sparse coding since no label was ha
RAYLEIGH_FADING
- Rayleigh fading is a statistical model for the effect of a propagation environment on a radio signal, such as that used by wireless devices. Rayleigh fading models assume that the magnitude of a signal that has passed through such a transmission
5
- BP学习算法逼近墨西哥草帽函数 在Windows环境中利用Matlab实现BP学习算法在达到期望均方误差最小的情况下正确表达墨西哥草帽函数。 实验目的:1.理解BP神经网络结构模型,初步了解BP网络的用途。 2.学习BP学习算法,掌握误差往回传播网络的构建思想。 3.能够正确使用BP学习算法表达墨西哥草帽函数。 -BP learning algorithm Mexican hat function approximation BP learning algorithm