matlabkuo

说明：  ：将信息论中熵的概念应用到特征选择中，定义了两种信息测度评价特征——误差熵和混叠熵，然后阐述了两种定义的不 用物理意义，分析了计算熵中最关键的区间划分问题，并提出一种较好的区间划分方法。(: The concept of entropy in information theory applied to feature selection, the definition of information measure evaluation of two features- error entropy and mixing entropy, and then describes the two definitions do not have the physical meaning of the calculation of entropy in the range of the most critical partitioning problem and propose a better method of interval division.)

对二维图像进行分数阶傅里叶变换，可用于图像去噪。(Fractional Fourier transform of the image, and can be used for image denoising.)

简易的录音功能。通过audiorecorder函数实现录音的功能。比电脑自带的录音好很多(Simple recording function. By recording audiorecorder function. Much better than the computer comes with the recording)

By designing in matlab GUI, this source code support for student to check four fundametal logic such as AND, NAND, OR, NOR in neural network.

均匀线阵方向图形成，包含没有波束指向和30°波束指向，加切比雪夫权值（旁瓣幅度相同）(Uniform linear array pattern formation, with no beam pointing and 30 ° beam pointing, Gache Chebyshev weights (the same side-lobe amplitude))

利用matlab编写的求解优化问题的分支定界算法，程序为函数形式可直接调用(Using matlab to solve optimization problems written in branch and bound algorithm, the program can be called directly as a function of the form)

说明：  本程序能够实现棒的横震动，而且在震动过程中能够计算出共振频率(This procedure can achieve the cross-bar vibration, but also in the process of vibration resonance frequency can be calculated)

说明：  根据语音信号的特点给出有关滤波器的性能指标：1）低通滤波器性能指标，fp=1000Hz，fc=1200 Hz， As=100dB，Ap=1dB；2）高通滤波器性能指标，fc=2800 Hz，fp=3000 Hz As=100dB，Ap=1dB；3）带通滤波器性能指标，fp1=1200 Hz，fp2=3000 Hz，fc1=1000 Hz，fc2=3200 Hz，As=100dB，Ap=1dB。用IIR对其分析，最后，利用MATLAB中的函数freqz画出各滤波器的频率响应。(This article according to the IIR filter and the FIR filter characteristic, uses the bilinearity method of transformation under the MATLAB bad boundary to design IIR and to design the FIR numeral filter separately with the window box number, and carries on the analysis to the gathering pronunciation signal, and finally gives IIR and FIR to the pronunciation filter effect.)

针对“BreastCancer”数据集，作为对比，第一次对特征集直接进行SVM分类，第二次使用粒子群算法进行特征选择后再进行SVM分类。并且对比和分析了两次分类的结果。(For "BreastCancer" data set, as a comparison, the first time the feature set is directly classified by SVM, and the second time the feature set is selected by particle swarm optimization before SVM classification. The results of the two classifications are compared and analyzed.)

TimeIntegrationMethods usin Molecular Dynamics