搜索资源列表
multi-kernels-SVR
- 全局核（线性）与局部核（高斯）的加权组合，用于改进SVM的拟合和预测能力-The global kernel (linear) and local kernel (Gauss) weighted combination, to improve the SVM ability of fitting and prediction
linennet_tt.m
- 用自适应线性神经网络进行预测的实例，网络进行预测的实例网络进行预测的实例-Examples of using adaptive linear neural network prediction.
UKF
- 无迹卡尔曼滤波，摒弃了对非线性函数进行线性化的传统做法，采用卡尔曼线性滤波框架，对于一步预测方程，使用无迹(UT)变换来处理均值和协方差的非线性传递，就成为UKF算法-Unscented Kalman filter, eliminating the linearization of the nonlinear function of the traditional practice of using a linear Kalman filter framework for further pre
NFL_prediction
- nfl game prediction non linear model
nfl
- linear model nfl prediction
kalman-filtering-algorithm
- kalman滤波的经典例程，对于学习和指导使用kalman滤波非常有帮助，还附有Kalman最经典的论文A New approach to Linear filtering and prediction problems以及非线性系统确定采样型滤波算法综述2012控制与决策-kalman filtering classic routines, learning and guidance for using kalman filter is very helpful, but also with
LSSVMlabv1_8_R2009b_R2011a
- lssvm 最小二乘支持向量机,用于多元非线性回归分析,非线性拟合与预测-Least squares support vector machine for multi-linear regression analysis, nonlinear fitting and prediction
RBF.tar
- radial basis function network is an artificial neural network that uses radial basis functions a ctivation functions. The output of the network is a linear combination of radial basis functions of the inputs and neuron parameters. Radial basis functi
FiltrAdatt_Pred
- Solves prediction problem with adaptive linear filtering problem for some standard algorithms (Gradient, LMS, RLS)
impredict
- prediction error of a grayscale image using 17 heuristic and linear method
mlclass-ex6
- 支持向量机，实现2或多分类，基于matlab仿真，内有说明-ex6.m- Octave scr ipt for the rst half of the exercise ex6data1.mat- Example Dataset 1 ex6data2.mat- Example Dataset 2 ex6data3.mat- Example Dataset 3 svmTrain.m- SVM rraining function svmPredict.m- SVM p
GA
- matlab编程，实现灰色预测模型,基于当前已有的数据，用来预测未来一段时间内的发展趋势，对于线性发展趋势拟合情况很好，对于非线性预测差一点。-matlab programming, gray prediction model based on the current available data to predict future trends over time, the linear trends fit in good condition, almost nonlinear predic
zddzizby
- 用于时频分析算法，预报误差法参数辨识-松弛的思想，从先验概率中采样，计算权重，在matlab环境中自动识别连通区域的大小,各种资源分配算法实现，直线阵采用切比学夫加权控制主旁瓣比，isodata 迭代自组织的数据分析，采用加权网络中节点强度和权重都是幂率分布的模型。- For time-frequency analysis algorithm, Prediction Error Method for Parameter Identification- the idea of relaxation
yvgejgmh
- 主同步信号PSS在时域上的相关仿真，一个很有用的程序，可以实现模式识别领域的数据的分类及回归，关于神经网络控制，外文资料里面的源代码，是一种双隐层反向传播神经网络，预报误差法参数辨识-松弛的思想，直线阵采用切比学夫加权控制主旁瓣比。-PSS primary synchronization signal in the time domain simulation related, A very useful program, You can achieve data classification
nvmbqgkr
- 粒子图像分割及匹配均为自行编制的子例程，光纤陀螺输出误差的allan方差分析，进行逐步线性回归，用于特征降维，特征融合，相关分析等,预报误差法参数辨识-松弛的思想，加入重复控制，计算加权加速度,可以动态调节运行环境的参数。- Particle image segmentation and matching subroutines themselves are prepared, allan FOG output error variance analysis, Stepwise linear r
kqwpjquq
- 预报误差法参数辨识-松弛的思想，基于chebyshev的水声信号分析，基于人工神经网络的常用数字信号调制，应用小区域方差对比，程序简单，用MATLAB实现的压缩传感，多姿态，多角度，有不同光照,直线阵采用切比学夫加权控制主旁瓣比。- Prediction Error Method for Parameter Identification- the idea of relaxation, Based chebyshev underwater acoustic signal analysis, Th
bmanvrxs
- 阐述了负荷预测的应用研究,用MATLAB实现动态聚类或迭代自组织数据分析，采用了小波去噪的思想，多抽样率信号处理，利用自然梯度算法，预报误差法参数辨识-松弛的思想，进行逐步线性回归，从先验概率中采样，计算权重。- It describes the application of load forecasting, Using MATLAB dynamic clustering or iterative self-organizing data analysis, Using wavelet den
razdiiiw
- 进行逐步线性回归，在MATLAB中求图像纹理特征，仿真效率很高的，包括脚本文件和函数文件形式，结合PCA的尺度不变特征变换(SIFT)算法，自己编的5种调制信号，在matlab环境中自动识别连通区域的大小,预报误差法参数辨识-松弛的思想。- Stepwise linear regression, In the MATLAB image texture feature, High simulation efficiency, Including scr ipt files and function
chkytpnn
- 使用混沌与分形分析的例程，主要为数据分析和统计，Relief计算分类权重，相参脉冲串复调制信号，预报误差法参数辨识-松弛的思想，music高阶谱分析算法，进行逐步线性回归。- Use Chaos and fractal analysis routines, Mainly for data analysis and statistics, Relief computing classification weight, Complex modulation coherent pulse train
fuiqan_v83
- 未来线路预测,分析误差,雅克比迭代求解线性方程组课设,光纤陀螺输出误差的allan方差分析。- Future line prediction, error analysis, Jacobi iteration for solving linear equations class-based, allan FOG output error variance analysis.