kernal_adaptive_filtering_box-1.3

nihe有关实验数据与参数的拟合，得到想要的参数与模型比较(nihe relevant experimental data and fitting parameters obtained with the model parameters you want to compare)

利用MATLAB仿真神经元的动作电位及各种电参数的变化，离子通道电导变化等(HH cells using Matlab simulation model of changes in all parameters and dynamics of Action Potential generation process)

leach protocol of wireless sensor network

自己编写的程序，能够实现单变量的线性回归，随机产生训练样本，利用梯度下降法实现训练过程，并且画出了误差的迭代曲线。(I have written a program that can achieve single-variable linear regression, random training sample using gradient descent method to achieve the training process, and draw the iterative curve errors.)

HBAT. pls use this in your assignment in multivariate analysis. credits to the owner thank you

polar plot for signal loss

genetic algorithm matlab

MATLAB与SIMULINK简要介绍，可以使用的pdf文件(MATLAB and SIMULINK brief)

ImageM integrates into a GUI several algorithms for interactve image processing and analysis. It allows to load images, apply filtering and/or segmentation, and run basic analyses. Interface is inspired the open source software ImageJ . To install, simply unzip, and add the path containing the +imagem directory. The application can be launched by typing: ImageM( rice.png ) (or any other image provided with the image processing toolbox). The development of ImageM started as a proof of concept for creating GUI dedicated to image processing. After adding several tools and options, I thought it could be useful to others, and maybe be used as an example of gui development. ImageM relies on the image processing toolbox and on the GUI Layout Toolbox. Most algorithms are wrapped into a specific Image class that allow to process in a unified way grayscale, binary and color images, 2D or 3D, and that can be used independently.

所谓阶梯式控制，就是对未来控制量的变化加以约束，强迫未来控制量以一阶指数响应曲线的形式变化。在这种控制策略下，可以避免求解控制律时的矩阵求逆，且在处理约束限制时，可方便将 和 的约束转化为对 的约束，减少了算法计算量。(The so-called ladder-type control, is the change in the future to control the amount of bound, forced to the future control the amount of the first order exponential response curve in the form of change. In this control strategy, the control law can be avoided when solving the matrix inversion, and the limit in dealing with constraints, and constraints can easily be translated into constraints, reducing the computational algorithm.)