MATLAB_gui_pass_parameters

一大学生编写的基于小波理论的图像重构和降噪演示，理论例程适合初学(image identification by wavelete )

Manual de Matlab 4.2 en castellano

关于VC与matlab混合编程的代码，包含众多例子，可以直接使用。(Matlab on the VC and mixed programming code, including many examples can be used directly.)

boot pv close loop pi controller

LDPC编码译码的Matlab代码，主要考察吞吐量随信噪比变化的关系。demo文件运行程序，其他文件为自己编的函数。(The LDPC codec Matlab code, mainly on the throughput changes with the signal-to-noise ratio. demo file to run the program, and other documents made themselves function.)

太阳能扰动观察法MPPT-simulink仿真，包含太阳能电池仿真及MPPT控制( U592A u803.0 u803 u803 u03C0 uBc2 u5BDF u6CD5MPPT-simulink u4EFF u771F uFF0C u5305 u542B u592A u9633 u80FD u7535 u6C60 u4EFF u771F u53CAMPPT u63A7 u5236)

简单直线阵列遗传算法优化，可以实现对简单四元直线阵列的优化。(Simple genetic algorithm to optimize linear array, which can realize the optimization of simple quarternary linear array.)

This is a GUI based system. The GUI is invoked from the command line by typing "ui_start". The GUI is self explanatory. Tool tips are given in blue color. The cursor needs to be positioned on these for detailed explanation. The main file is "runsim.m". In these simulations the SISO option is not explicitly given. This is because the SISO result for a particular type of modulation is shown in every plot for purposes of comparison. This software has been tested on MATLAB version 6.0 and above with signal processing and communication toolbox options.(This is a GUI based system. The GUI is invoked from the command line by typing " ui_start" . The GUI is self explanatory. Tool tips are given in blue color. The cursor needs to be positioned on these for detailed explanation. The main file is " runsim.m" . In these simulations the SISO option is not explicitly given. This is because the SISO result for a particular type of modulation is shown in every plot for purposes of comparison. This software has been tested on MATLAB version 6.0 and above with signal processing and communication toolbox options.)

sssc simulink file of matlab

Training Artificial Neural Network. XOR Problem. Summation Units, Log-Sigmoid Neurons with Biases. Input Layer: 2, Hidden Layer: 2, Output Layer: 1 neurons. Returns mean square error between desired and actual outputs. Reference Papers: D. Karaboga, B. Basturk Akay, C. Ozturk, Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed-Forward Neural Networks, LNCS: Modeling Decisions for Artificial Intelligence, 4617/2007, 318-329, 2007. D. Karaboga, C. Ozturk, Neural Networks Training by Artificial Bee Colony Algorithm on Pattern Classification, Neural Network World, 19(3), 279-292, 2009. */