Description: 灰色预测模型称为CM模型，G为grey的第一个字母，M为model的第一个字母。GM（1，1）表示一阶的，一个变量的微分方程型预测模型。GM（1，1）是一阶单序列的线性动态模型，主要用于时间序列预测。 一、GM（1，1）建模 设有数列 共有 个观察值 对 作累加生成，得到新的数列 ，其元素 （5-1） 有： 对数列 ，可建立预测模型的白化形式方程， （5-2） 式中： ——为待估计参数。分别称为发展灰数和内生控制灰数。设 为待估计参数向量 则 按最小二乘法求解， 有： （5-3） 式中： （5-4） （5-5） 将（5-3）式求得的 代入（5-2）式，并解微分方程，有 （1，1）预测模型为： （5-6）-gray forecasting model called CM model, the G-gray of a letter, the M model for the first letter. GM (1,1), a band of a variable type of differential equation models. GM (1,1) is a sequence of single-band linear dynamic model, mainly for time series prediction. A GM (1,1) model series with a total value of observation for the cumulative production, to a new series of its elements (5-1) : The series, we can establish the prediction model albino form of equation (5-2) where :-- to question the estimated parameters. The development will be known as the gray and hygiene control within a few gray. Set parameters to be estimated according to Vector least squares method, are : (5-3) where : (5-4) (5-5) (5-3) that obtained in lieu of income (5-2)- and solutions differential equations, (1 1) Fore Platform: |
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Author:罗军 |
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Description: 实现M=4,8 PSK星设图
M=4的PSK系统，分别取信噪比为odB、10dB和20dB，在星座图上观察接收端接收到的信号向量。-achieve M = 4,8 PSK-based map of M = 4 PSK system, the signal-to-noise were taken odB. 10dB and 20dB, in the constellation observation receiving termination received signal vector. Platform: |
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Author:xoaxiao |
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Description: 卡尔曼滤波的MATLAB实现，是一个教程事例-updates a system state vector estimate based upon an observation, using a discrete Kalman filter. Platform: |
Size: 3072 |
Author:liangbo |
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Description: 著名的信号处理算法卡尔曼滤波器的Matlab源代码。-KALMANF- updates a system state vector estimate based upon an
observation, using a discrete Kalman filter. Platform: |
Size: 3072 |
Author:Zachery Chen |
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Description: hmm文件时运用HMM算法实现噪声环境下语音识别的。其中vad.m是端点检测程序；mfcc.m是计算MFCC参数的程序；pdf.m函数是计算给定观察向量对该高斯概率密度函数的输出概率；mixture.m是计算观察向量对于某个HMM状态的输出概率，也就是观察向量对该状态的若干高斯混合元的输出概率的线性组合；getparam.m函数是计算前向概率、后向概率、标定系数等参数；viterbi.m是实现Viterbi算法；baum.m是实现Baum-Welch算法；inithmm.m是初始化参数；train.m是训练程序；main.m是训练程序的脚本文件；recog.m是识别程序。-hmm HMM algorithm file using speech recognition in noisy environments. Which is the endpoint detection process vad.m mfcc.m procedure is to calculate the MFCC parameters pdf.m function is calculated for a given observation vector of the Gaussian probability density function of output probability mixture.m is to calculate the observation vector for a HMM state output probability of observation vector is the number of Gaussian mixture per state output probability of the linear combination getparam.m before the calculation of the probability function, backward probability, calibration coefficients and other parameters viterbi.m is Viterbi algorithm implementation baum.m Baum-Welch algorithm to achieve inithmm.m is the initialization parameters train.m is the training program main.m training program is a script file recog.m is to identify procedures. Platform: |
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Author:于军 |
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Description: KALMANF - updates a system state vector estimate based upon an
observation, using a discrete Kalman filter.
Version 1.0, June 30, 2004
This tutorial function was written by Michael C. Kleder
INTRODUCTION
Many people have heard of Kalman filtering, but regard the topic
as mysterious. While it s true that deriving the Kalman filter and
proving mathematically that it is "optimal" under a variety of
circumstances can be rather intense, applying the filter to
a basic linear system is actually very easy. This Matlab file is
intended to demonstrate that.
An excellent paper on Kalman filtering at the introductory level,
without detailing the mathematical underpinnings, is:
"An Introduction to the Kalman Filter"
Greg Welch and Gary Bishop, University of North Carolina- KALMANF - updates a system state vector estimate based upon an
observation, using a discrete Kalman filter.
Version 1.0, June 30, 2004
This tutorial function was written by Michael C. Kleder
INTRODUCTION
Many people have heard of Kalman filtering, but regard the topic
as mysterious. While it s true that deriving the Kalman filter and
proving mathematically that it is "optimal" under a variety of
circumstances can be rather intense, applying the filter to
a basic linear system is actually very easy. This Matlab file is
intended to demonstrate that.
An excellent paper on Kalman filtering at the introductory level,
without detailing the mathematical underpinnings, is:
"An Introduction to the Kalman Filter"
Greg Welch and Gary Bishop, University of North Carolina Platform: |
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Author:mabaiwang |
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Description: 计盒分形维数计算的matlab源代码编程实现过程-Function [Fdgen NoisfreFD ]=fractaldim(y,Q,M)
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Usage: Computes fractal dimension by box counting(BC) method.
NOTE: Running the code may take a little time, because it calculates
dimensions for all embedding dimensions up to M.
This code is based on an algorithm that constructs a box for the first
observation and for other observations test which it belongs to previous
box(s). If the observation belongs to one of the existent boxes then
increases number of points in the relevant box,otherwise makes a new box. the algorithm of this code uses only rounding down and it does not need
binary coding, sorting and so on. It uses only transformation data to [0,2^32-1] and finding valid boxes based on Leibovich and Toth(1989). Inputs: y is a vector time series. q stands for generalized dimension q=1 entropy dimension,q=2 correlation dimension and so on. q=0 is box counting dimen Platform: |
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Author:panwangsheng |
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Description: K-means聚类算法的matlab实现(k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.) Platform: |
Size: 1024 |
Author:invoker`Z |
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Search - observation vector matlab