PSO

独立成分分析（ICA）是最近开发的方法，它的目标是找到一种nongaussian等数据的线性表示 该元件是统计独立的或尽可能独立。应用包括信号特征提取与分离数据的基本结构。 在本文中，我们提出的基本理论和ICA的应用，以及我们对这个问题的近期工作。(Independent component analysis (ICA) is a recently developed method in which the goal is to find a linear representation of nongaussian data so that the components are statistically independent, or as independent as possible. Such a representation seems to capture the essential structure of the data in many applications, including feature extraction and signal separation. In this paper, we present the basic theory and applications of ICA, and our recent work on the subject)

典型飞机目标的RCS仿真，内容丰富，分析较为仔细(Typical aircraft target RCS simulation, rich in content, more careful analysis)

用MATLAB实现瑞利信道下的误比特率仿真(Using MATLAB to achieve Fading Channel BER simulation)

用于近场信号的参数估计，如信号方位、距离(estimation of DOA)

一个基于神经网络的手写体数字识别的matlab程序，可以自行进行神经网络训练并识别给出相应的结果(A neural network-based handwritten numeral recognition matlab program, the neural network can be trained to identify themselves and give the corresponding results)

并网下垂控制仿真，单台并网逆变器，采用下垂控制(Simulation of grid-connected sag control, single grid-connected inverter, using sag control)

能产生信号如正弦，方波，锯齿波等,并进行合成分解等功能(Can generate signals such as sine, square, sawtooth, etc., and the synthesis of functional decomposition)

VC与matlab混合编程的例子 是一个间谐波处理程序，滤波算法用matlab 界面用VC(VC and matlab programming examples of hybrid between a harmonic process, filtering algorithm matlab interface with VC)

Support Vector Cla s sification and Gaus sian Kernel with Multiple Widths

Find the theorical capacity of an optical comb sistem