搜索资源列表
svcm_run
- support vector classification machine ％ soft margin ％ uses \"kernel.m\" ％ ％ xtrain: (Ltrain,N) with Ltrain: number of points N: dimension ％ ytrain: (Ltrain,1) containing class labels (-1 or +1) ％ xrun: (Lrun,N) with Lrun: number of points
netlist_CH1
- As a consequence, more exact models of devices can be retained for analysis rather than the approximate models commonly introduced for the sake of computational simplicity. A computer icon appears in the margin with each introduction of MATLAB
netlist_CH2
- As a consequence, more exact models of devices can be retained for analysis rather than the approximate models commonly introduced for the sake of computational simplicity. A computer icon appears in the margin with each introduction of MATLAB
netlist_CH3
- As a consequence, more exact models of devices can be retained for analysis rather than the approximate models commonly introduced for the sake of computational simplicity. A computer icon appears in the margin with each introduction of MATLAB
netlist_CH4
- As a consequence, more exact models of devices can be retained for analysis rather than the approximate models commonly introduced for the sake of computational simplicity. A computer icon appears in the margin with each introduction of MATLAB
netlist_CH5
- As a consequence, more exact models of devices can be retained for analysis rather than the approximate models commonly introduced for the sake of computational simplicity. A computer icon appears in the margin with each introduction of MATLAB
ugm
- Upper gain margin Intended for a senior-level course on the analysis and design of digital control systems, the text is also useful for graduate students and practicing engineers who are learning state-space design techniques.
kechshjisuidongxitong
- 已知某控制系统结构图如下所示,要求设计校正环节G(s),使系统对于阶跃输入的稳态误差为零。使系统校正后的相角裕量γ≥45°，幅值裕量kg≥10db。-Known structure of a control system as shown below, links to design correction G (s), allowing the system to step input for the steady-state error to zero. The system after c
MATLAB
- 数值分析的实验程序，实现了牛顿迭代、样条差值等三种差值等数值分析方法的仿真。-Numerical Analysis of the experimental procedure, the realization of the Newton iteration, the margin three-spline difference numerical analysis methods such as simulation.
chapter5_45
- 这是模式分类一书中第五章的固定增量感知器和带裕量的变增量感知器的matlab代码实现。-This is the pattern classification of a book chapter V and the fixed-increment perceptron with margin perceptron incremental change of the matlab code.
chapter5_68
- 这是模式分类一书中批处理裕量松弛算法和单样本的裕量松弛算法的matlab代码实现。pudn绝无二家。-This is a book of pattern classification margin batch relaxation algorithm and the margin of a single sample relaxation algorithm matlab code. 2 pudn no.
Interpolation
- 这个是matlab经典教程的一个关于差值的源代码，请大家下载，-This is a classic tutorial matlab on the margin of the source code, please download,
MatlabGUI
- Matlab GUI编程学习资料，网上搜刮，希望对大家有所帮助-Matlab classic algorithms, including data analysis, solving equations, plotting, fitting the margin, planning and other commonly used content
vsk_camera_vop_Nearest_Neighbor_Replication
- 这是对Bayer图像进行差值还原彩色图像的MATLAB模型文件-This is a Bayer image to restore the margin of the MATLAB model of color image files
chazhi
- 用来对数据进行差值计算，并用来进行作图 用来对数据进行差值计算，并用来进行作图-The data used to calculate the margin, and used for mapping
fenduanxianxingchazhi
- 分段线性样条差值的matlab仿真，用于数值计算-Piecewise linear spline margin matlab simulation, for the numerical calculation
lagraner
- 差值问题中的拉格朗日插值的MATLAB源程序-Issue of the margin of the Lagrange interpolation of the MATLAB source code
ZYP11
- 四、软件的功能 1、核算结算成本 2、稽核县局收入 3、统计六网的收入和成本 4、核算利润率 5、统计客户经理业绩-4, the software features a, accounting settlement cost of the two, the auditing county bureau of income 3, Statistics 6 net revenue and cost four, accounting profit margin of 5,
lagc
- 求系统串联滞后校正器传递函数的函数 ，其中sope是系统的开环传递函数，当key=1时，var为gama，是根据要求校正后的相角 稳定裕度计算之后校正器；当key=2时，var=wc，则是 根据要求校正后的剪切频率计算校正器。-Demand system is connected lag compensator transfer function of the function, which sope is the system open-loop transfer functi
icml2011-code
- This a reference implementation for the synthetic experiments on lower linear envelope inference and learning described in "Max-margin Learning for Lower Linear Envelope Potentials in Binary Markov Random Fields", Stephen Gould, ICML 2011