switch2

贝叶斯网络matlab程序，最新的版本的程序(bayesian network matlab)

These set of programs are written to design a robust power system stabilizer for minimizing the effects of low frequency oscillations in electric power systems. A complete nonlinear model of the power system represented by a single machine connected to an infinite bus is developed in the Simulink environment. A fuzzy Power System Stabilizer is designed using the fuzzy logic toolbox of matlab and its parameters are tuned by a NEural Network controller.

用MATLAB实现数值分析中插值与曲线拟合(Numerical analysis in MATLAB realization of the interpolation and curve)

运用禁忌搜索算法解决旅行商问题TSP的程序代码，简单实用(yunyon gjin jisou suosaulv xign shang)

Codes .M object detection with matlab. Create the list of detections D for the images referenced in the G list Tickets Detector File contains the address of the detector file, Xml extension G List of rectangles of objects that are really in the images Visualization is a var. Boolean that enables or disables the display of The detected rectangles and the real objects in the images. Departures D List of detections rectangles Note 1: G and D are a standard structure for listing rectangles Field 1: imageFilename Field 2: objectBoundingBoxes Note 2: press spacebar to jump to the next image

提供matlab的所有命令及相关的函数介绍(The matlab command and related functions)

Simulating ASK and BPSK and more

用PSO优化LSSVM的参数，得到最优参数，使得分类更加准确。(Using the PSO the LSSVM the parameter, the optimal parameters, more accurate classification. )

Some algorithms of variable step size LMS adaptive filtering are studied．The VS—LMS algorithm is improved． Another new non-linear function between肛and e(／ t)is established．The theoretic analysis and computer simulation results show that this algorithm converges more quickly than the origina1．Furthermore，better antinoise property is exhibited under Low—SNR environment than the original one．(variable step size of a LMS daptive filtering are studied. The VS-LMS algorithm is improved. Another new non-linear function between anus and e (/ t) is established. The theoretic analysis and computer simulatio n results show that this algorithm converges mo 're quickly than the origina1. Furthermore, better antinoise property is exhibited under L ow- SNR environment than the original one.)

Texture classifier using wavelet