搜索资源列表
11
- L-M优化算法（trainlm）和贝叶斯正则化算法（trainbr）
BPtrainlmtrainbr.rar
- 用L-M 优化算法与贝叶斯正则化算法训练同一个样本,By LM optimization algorithm with Bayesian regularization algorithm for training with a sample
Code_MATLAB_Optimization
- 这是龚纯《精通MATLAB最优化计算》随书源码(M文件)。基于MATLAB优化工具箱，代码包含的内容有：牛顿法等无约束一维极值问题、单纯形搜索法等无约束多维极值问题、Rosen梯度投影法等约束优化问题、L-M法等非线性最小二乘优化问题、线性规划、整数规划、二次规划、粒子群优化、遗传算法。-This is pure Gong " Mastering MATLAB optimization calculations," with the book source (M file)
LLR_compute
- 由4个.m文件组成，用于16QAM和64QAM调制的软信息的提取-By 4. M files for 16QAM and 64QAM modulation of the soft information extraction
WaveletBasedTextureRetrievalUsingGeneralizedGaussi
- 基于小波变换的特征检索算法，用了广义高斯函数和K-L距离为相似侧度 -Wavelet-Based Texture Retrieval Using Generalized Gaussian Density and Kullback–Leibler Distance
MATLABoptimization
- matlab最优化程序包括 无约束一维极值问题 进退法 黄金分割法 斐波那契法 牛顿法基本牛顿法 全局牛顿法 割线法 抛物线法 三次插值法 可接受搜索法 Goidstein法 Wolfe.Powell法 单纯形搜索法 Powell法 最速下降法 共轭梯度法 牛顿法 修正牛顿法 拟牛顿法 信赖域法 显式最速下降法， Rosen梯度投影法 罚函数法 外点罚函数法 內点罚函数法 混合罚函数法 乘子法 G－N法 修正G－N法 L－M法
G-N
- 此文件包含用G-N法，修正G-N法，L-M法，Lsqnonlin求解非线性最小二乘优化问题MATLAB源代码，请大家参考-This file contains the method with the GN, GN law amendment, LM Law, Lsqnonlin MATLAB nonlinear least squares optimization source code, please refer to
BP1
- 采用L-M优化算法用以训练BP神经网络，使其能够拟合某一附加有白噪声的正弦样本数据。设计一个三层BP神经网络，其网络的隐含层神经元的激励函数为双曲正切型，输出层各神经元的激励函数为线性函数，隐含层有6个神经元-LM optimization algorithm used to train BP neural network to enable it to fit a white noise, sine additional sample data. Design a three BP neura
Nonlinear_Matlab.m
- 非线性最小二乘法Matlab实现 f=x(1)*K^x(2)*L^x(3)-b Cobb-Douglas生产函数-Matlab nonlinear least square method to achieve f = x (1)* K ^ x (2)* L ^ x (3)-b Cobb-Douglas production function
l_curve
- l_curve.m L曲线准则-l_curve.m L_curve principle............
L-M
- L-M,BP网络算法，用matlab语言编译-LM, BP network algorithm with matlab compiler
trainlm
- 采用两种训练方法，即 L-M 优化算法（trainlm）和贝叶斯正则化算法（trainbr）-Using two training methods, namely, LM optimization algorithm (trainlm) and Bayesian regularization algorithm (trainbr)
Sylvester_Char_Polynomial_Var1.m
- Sylvester equations solving via the characteristic polynomial approach- using Sylvester operator s characteristic polynomial L
plot3d_2
- This function produces an image of a 3D object defined by matrix a(l,m,n) in terms of voxels the image is a view after rotating the object by angles alfa and beta (in degree) b is the image and d is its ditance to the viewer matrix The first figure d
LU_factor.m
- Method with Gaussian Elimination without Pivoting LU factorization of matrix A using Gaussian-elimination without pivoting Inputs : A --> n x n matrix Outputs : L (lower triangular) && U (upper triangular) - Method with Gaussi
lu_pivot.m
- Method with Gaussian Elimination with Pivoting function [L,U,P] = lu_pivot(A) - Method with Gaussian Elimination with Pivoting function [L,U,P] = lu_pivot(A)
L-Msuanfa
- matlab实现的L-M算法的函数拟合，可以用了进行函数的拟合-matlab implementation of the LM function fitting algorithm can be used for the fitting function
kmeans
- function [L,C] = kmeans(X,k) KMEANS Cluster multivariate data using the k-means++ algorithm. [L,C] = kmeans(X,k) produces a 1-by-size(X,2) vector L with one class label per column in X and a size(X,1)-by-k matrix C containing the centers
pcm_quan_enco
- file Matlab abc xyz a b c d e f x y z d h i k l m n s
L-m
- l_m 算法 matlab实现