m_generate

里面有大量的Matlab的应用实例，可以给大家的学习带来帮助，它不仅适合初学者而且也可以提高大家的Matlab编程能力。(Which a large number of Matlab application example, can help to bring everyone to learn, not only for beginners but also can improve our ability to Matlab programming.)

基于频带方差的语音端点检测，很有用的，大家自己好好看，不是太难(variance based on the frequency of speech endpoint detection, very useful, we all do well, not too difficult)

A Hopfield network is a form of recurrent artificial neural network invented by John Hopfield in 1982. Hopfield nets serve as content-addressable memory systems with binary threshold nodes. They are guaranteed to converge to a local minimum, but convergence to a false pattern (wrong local minimum) rather than the stored pattern (expected local minimum) can occur. Hopfield networks also provide a model for understanding human memory.

these m files contain the solution of some problems in the book "communication system" by Haykin

利用matlab进行信号采样（欠采样、过采样等）的源程序(Sampling the signal using matlab (less sampling, oversampling, etc.) of the source)

此文件是传递函数拉氏变换MATLAB应用(Laplace transform of the transfer function)

用matlab构建的信号发生器，其中包括交互界面，产生各种简单的波形，以及波形的混合叠加，以及外加音频的简单处理。交互界面中可以清楚看到波形的时域和频域的波形，包括波形的幅值，周期，功率均有图形显示(Constructed using matlab signal generator, including interface, simple treatment produces a variety of simple waveforms, and mixed superimposed waveforms, as well as external audio. Interactive interface, you can clearly see the waveform frequency domain and time domain waveforms, including the waveform amplitude, period, power has a graphic display )

三步梯形相移图案的matlab生成程序，使用的是灰度图像。(Three-step phase shift pattern matlab trapezoid generation program, using a gray-scale image.)

Image compression using SVD is a very useful compression algorithm. The image size will not decrease in the compression algorithm.

说明：  径向基函数神经网络的优点：逼近能力，分类能力和学习速度等方面都优于BP神经网络，结构简单、训练简洁、学习收敛速度快、能够逼近任意非线性函数，克服局部极小值问题。原因在于其参数初始化具有一定的方法，并非随机初始化。代码为RBF的matlab程序，可以使用但是需要自己配置数据。(Radial Basis Function Neural Network (RBF-NN) has the advantages of better approximation ability, classification ability and learning speed than BP Neural Network (BP Neural Network). It has simple structure, simple training, fast learning convergence, and can approximate any non-linear function to overcome the local minimum problem. The reason is that its parameter initialization has a certain method, not random initialization.)