work

[mimi,i]=min(abs(imag(disp1)-imag(disp2))) legend( LHS of dispersion relation , RHS of dispersion equation ) xlabel( u,cl ) figure(1) clc uppercuts = input( Enter number of intersection points desired: ) [g,h] = ginput(uppercuts) w = sqrt(V.^2-u.^2) s = sprintf( Constants: ) disp(s) for p = 1:uppercuts s = sprintf( u( d d) = 0.10f ,L-1,p,u(p)) disp(s) s = sprintf( w( d d) = 0.10f ,L-1,p,w(p)) disp(s)

改代码为基于Konhone网络的分类算法，是MATLAB人工智能的经典算法(Change the code based classification algorithm Konhone networks, artificial intelligence is a classical algorithm MATLAB)

说明：  变分模态分解算法是2014年新提出的，广泛应用于信号处理领域。 VMD分解的子程序，给定分解的时域信号，数据保真约束平衡参数、噪声波峰时间步长、VMD分解个数、迭代精度可以得到 分解的子分量、子分量的波谱、各模式的中心频率。(The variational mode decomposition (VMD) algorithm was proposed in 2014, which is widely used in the field of signal processing. Given the time domain signal decomposed by VMD, the data fidelity constraint balance parameter, the time step of noise peak, the number of VMD decomposition and the iteration accuracy, the decomposed sub components, the spectrum of sub components and the center frequency of each mode can be obtained.)

Matlab for digital communication

说明：  高斯消去法 用列主元求解AX=b 数值计算实习 例子(Gaussian elimination)

PID控制算法简单、鲁棒性强，但其参数整定过程繁琐，整定时需要控制对象的精确数学模型，而且整定往往是针对某一种具体工况进行的，缺乏自学习和自适应能力。模糊神经网络则兼备了模糊逻辑和神经网络的优点，具有函数逼近功能，具有较强的自适应、自学习能力、容错能力和泛化能力。借助于遗传算法对全局性参数进行优化设计，借助于BP算法对局部性参数进行优化，将模糊神经网络和遗传算法引入PID控制参数的整定过程，构造出一种基于模糊神经网络和遗传算法的智能PID控制器(Intelligent controller based on fuzzy neural network and genetic algorithm)

：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.)

in this mfile i have wrote the principal component analysis algorithm in matlab.

说明：  应用MATlAB语言处理数字信号与数字图像(Application MATlAB language processing digital signals and digital images)

机器人开发工具箱，matlab程序，是一个压缩文件(Robot Development Kit)