music-algorithm

直流电机调速系统仿真，建立模型，系统仿真，画出仿真曲线(DC motor speed control system simulation, modeling, system simulation, simulation curve drawn)

在自适应控制中，用Matlab算法实现广义最小方差控制(In adaptive control, using Matlab algorithm generalized minimum variance control)

matlab基本信号处理函数相关资料（pdf格式）(matlab basic signal processing functions related information (pdf format))

单级倒立摆控制 此程序用在过程控制中，仿真软件为Matlab(Single Link Inverted Pendulum Control)

algorithm to calculat the Lyapunov exponent from time series.

matlab code for clastering

：该文把局部三值模式(Local Ternary Patterns, LTP)纹理特征引入Mean Shift 跟踪算法，提出了基于多 特征的Mean Shift 人脸跟踪算法以解决Mean shift 跟踪算法的鲁棒性问题。通过对LTP 纹理特征的分析、研究， 提出了一个LTP 关键纹理模型，既增强了目标的关键纹理信息，又简化了LTP 纹理模型。在此基础上，提出一 种基于LTP 关键纹理特征和肤色特征的Mean Shift 人脸跟踪算法，有效地解决了Mean Shift 算法的鲁棒性问题。 为进一步提高对快速运动目标的跟踪速度和跟踪性能，该文引入了卡尔曼滤波器对目标进行预测。实验结果表明， 该文的算法在目标定位的准确性和跟踪性能上比Mean Shift 算法均有明显的提高。(: In this paper, the texture characteristics of the local ternary patterns Local Ternary Patterns (LTP) Mean Shift tracking algorithm proposed Mean Shift face tracking algorithm based on multiple features in order to solve the Mean shift tracking algorithm robustness. Texture characteristics of LTP analysis, research, a key LTP texture model, not only enhanced the key goal of texture information, but also simplifies the the LTP texture model. On this basis, based on the LTP key texture features and color characteristics Mean Shift face tracking algorithm, effectively solved the robustness of the Mean Shift algorithm. To further enhance the fast-moving target tracking speed and tracking performance, this paper introduces the Kalman filter to predict the target. The experimental results show that the algorithm of the text in the target positioning accuracy and tracking performance than the Mean Shift algorithm to significantly improve.)

matlab课件很有用的,对matlab学习很有帮助,希望采纳谢谢(it s very useful)

生成雷达回波的matlab源程序，包括噪声，线性调频等相关内容，详细注释(Generate radar echo matlab source code, including noise, chirp, detailed notes)

< 深入浅出MATLAB 7_X混合编程>>电子书,系统介绍了matlab和其他语言的混合编程,和交互接口,如C/C++ fortran等!深入浅出,很值得推荐()