Description: 求解大型稀疏方程组的全选主元高斯-约当消去法--返回零表示原方程组的系数矩阵奇异，返回的标志值不为零，则表示正常返回。-solving large sparse linear system-wide elections PCA Gauss-Jordan elimination method -- to return to the original equation is expressed by the coefficient matrix, a sign of the return value is not zero, then returned to normal. Platform: |
Size: 913 |
Author:陈益林 |
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Description: 求解大型稀疏方程组的全选主元高斯-约当消去法--返回零表示原方程组的系数矩阵奇异，返回的标志值不为零，则表示正常返回。-solving large sparse linear system-wide elections PCA Gauss-Jordan elimination method-- to return to the original equation is expressed by the coefficient matrix, a sign of the return value is not zero, then returned to normal. Platform: |
Size: 1024 |
Author:陈益林 |
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Description: MIMO系统的典型zf均衡器的算法C＋＋实现。brief ZF Equalizer, i.e. Matrix inversion and multiplication
block, i.e. the solution of a linear equation system.-Zf typical MIMO systems equalizer algorithm C to achieve. brief ZF Equalizer, ie Matrix inversion and multiplicationblock, ie the solution of a linear equation system. Platform: |
Size: 6144 |
Author:吴 昊 |
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Description: The main features of the considered identification problem are that there is no an a priori separation of the variables into inputs and outputs and the approximation criterion, called misfit, does not depend on the model representation. The misfit is defined as the minimum of the l2-norm between the given time series and a time series that is consistent with the approximate model. The misfit is equal to zero if and only if the model is exact and the smaller the misfit is (by definition) the more accurate the model is. The considered model class consists of all linear time-invariant systems of bounded complexity and the complexity is specified by the number of inputs and the smallest number of lags in a difference equation representation. We present a Matlab function for approximate identification based on misfit minimization. Although the problem formulation is representation independent, we use input/state/output representations of the system in order -The main features of the considered identification problem are that there is no an a priori separation of the variables into inputs and outputs and the approximation criterion, called misfit, does not depend on the model representation. The misfit is defined as the minimum of the l2-norm between the given time series and a time series that is consistent with the approximate model. The misfit is equal to zero if and only if the model is exact and the smaller the misfit is (by definition) the more accurate the model is. The considered model class consists of all linear time-invariant systems of bounded complexity and the complexity is specified by the number of inputs and the smallest number of lags in a difference equation representation. We present a Matlab function for approximate identification based on misfit minimization. Although the problem formulation is representation independent, we use input/state/output representations of the system in order Platform: |
Size: 1031168 |
Author:kedle |
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Description: A Matlab toolbox for exact linear time-invariant system identification is presented. The emphasis is on the variety of possible ways to implement the mappings from data to parameters of the data generating system. The considered system representations are input/state/output, difference equation, and left matrix fraction.
KEYWORDS: subspace identification, deterministic subspace identification, balanced model reduction, approximate system identification, MPUM.
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Size: 92160 |
Author:kedle |
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Description: 一个画一种Logistic Map 分岔图(bifurcation diagram)的程序，运行后，你们可以看到它与常规的非线性系统的行为不一样。该映射可以用如下方程表述:
xn=1-a*x2n-1
其中，a――[0,2].
-Logistic Map a painting of a bifurcation diagram (bifurcation diagram) procedures, running, you can see it with the conventional non-linear system behavior not the same. The mapping equation can be expressed as follows: xn = 1-a* x2n-1 of them, a- [0,2]. Platform: |
Size: 1024 |
Author:潘水洋 |
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Description: this is a course ware used in US university-this is about system of simutaniously linear algebra equation and it is based on matlab Platform: |
Size: 311296 |
Author:Xiao Yang |
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Description: 介绍基于神经网络的反馈线性化控制过程。反馈线性就是利用反馈的控制手段来消除系统中的非线性，以使的其闭环系统的动力学方程是线性的。-Introduced based on neural network feedback linearization control process. Feedback linearization is to use feedback control to eliminate the non-linear system, so that its closed-loop system dynamic equation is linear. Platform: |
Size: 1024 |
Author:白雪静 |
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Description: duct.f computes a fully developed viscous laminar flow in a rectangular channel of aspect ratio b/a (=bar).
solve the equation:
d^2u/dx^2 + d^2u/dy^2 = -1, -a<x<a, -b<y<b
method of discretisation: 3-point centered differences
method of linear system solution: gauss-seidel + sor
-duct.f computes a fully developed viscous laminar flow in a rectangular channel of aspect ratio b/a (=bar).
solve the equation:
d^2u/dx^2+ d^2u/dy^2 =-1,-a<x<a,-b<y<b
method of discretisation: 3-point centered differences
method of linear system solution: gauss-seidel+ sor
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Size: 2048 |
Author:Barboy |
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Description: 数值积分方法练习与函数调用：利用ode23或ode45求解线性时不变系统微分方程
=Ay(t)并绘出y（t）的曲线，式中
=0， =4
-Using ode45 ode23 or to solve the linear differential equation when the same system
= Ay (t) and draw y (t) curve, type
= 0, = 4
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Size: 36864 |
Author:王丽丽 |
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Description: 灰色系统理论相关源代码（Matlab语言）目前使用最广泛的灰色预测模型就是关于数列预测的一个变量、一阶微分的GM(1,1)模型。它是基于随机的原始时间序列，经按时间累加后所形成的新的时间序列呈现的规律可用一阶线性微分方程的解来逼近。经证明，经一阶线性微分方程的解逼近所揭示的原始时间序列呈指数变化规律。因此，当原始时间序列隐含着指数变化规律时，灰色模型GM(1,1)的预测是非常成功的。-Gray system theory, source code (Matlab language) is the most widely used gray forecasting model is a variable sequence prediction, the first derivative of GM (1,1) model. It is based on the original time series of random law presented by the new time series formed by the accumulated time first-order linear differential equations to approximate. Proved that the original time series, the first-order linear differential equation approximation revealed an exponential variation. Therefore, when the original time series implicit index variation, gray model GM (1,1) prediction is very successful. Platform: |
Size: 1024 |
Author:小二 |
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Description: 灰色系统理论相关源代码（Matlab语言）目前使用最广泛的灰色预测模型就是关于数列预测的一个变量、一阶微分的GM(1,1)模型。它是基于随机的原始时间序列，经按时间累加后所形成的新的时间序列呈现的规律可用一阶线性微分方程的解来逼近。经证明，经一阶线性微分方程的解逼近所揭示的原始时间序列呈指数变化规律。因此，当原始时间序列隐含着指数变化规律时，灰色模型GM(1,1)的预测是非常成功的。-Gray system theory, source code (Matlab language) is the most widely used gray forecasting model is a variable sequence prediction, the first derivative of GM (1,1) model. It is based on the original time series of random law presented by the new time series formed by the accumulated time first-order linear differential equations to approximate. Proved that the original time series, the first-order linear differential equation approximation revealed an exponential variation. Therefore, when the original time series implicit index variation, gray model GM (1,1) prediction is very successful. Platform: |
Size: 1024 |
Author:小二 |
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Description: 卡尔曼滤波(Kalman filtering)一种利用线性系统状态方程，通过系统输入输出观测数据，对系统状态进行最优估计的算法。本程序使用opencv进行卡尔曼滤波。-Kalman filter (Kalman filtering) A linear system equation of state, observation data input and output through the system, the system state optimal estimation algorithms. This program uses opencv Kalman filtering. Platform: |
Size: 7168 |
Author:赵鹏 |
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