yichuansuanfayouhua

独立成分分析( I C A) 是一项把混合信号分解成具有统计独立性成分的新技术 。I C A近年已在生物医 域的信号分离中展示 了很好的应用前景 。 我们比较系统地介绍了 I C A的基本原理 、 主要算法 、 应用和 究的发展方向，旨在进一步推动有关的理论与应用研究工作。 (Independent Component Analysis (ICA) is a decomposition of the mixed-signal components into a statistical independence of the new technologies. In recent years, ICA has been in bio-medical domain signal separation show a very good application prospects. We systematically introduced the basic principle of ICA, the main algorithms, applications and the development direction of research aimed at further promoting the application of the theory and research.)

自适应波束成形室内模型matlab的mimo技术仿真(Adaptive beam forming model Matlab MIMO technology simulation )

现代智能算法法程序代码，遗传法 贪婪法 模拟退火法 蚂蚁群法 回溯法(Modern intelligent algorithms law code, the genetic method simulated annealing greedy algorithm backtracking ant colony method)

扩展卡尔曼滤波算法以及不敏卡尔曼滤波算法的比较(the comparison between EKF and UKF)

matlab coding of bpsk

em算法运用于有散射体的信道模型下实现参数估计（los距离）(em algorithm applied to a scattering channel model to achieve parameter estimation (los distance))

汽车车牌的图像识别。 根据车牌凸角轮廓特征，采用MPP算法进行车牌倾斜校正；采用彩色图像分割方法，最大限度的去除图像噪声干扰；设计了基于神经网络的多种彩色背景车牌字符识别方法，取得了良好的效果。(Image recognition of vehicle license plates. According to plate convex contour feature, the use of MPP plate tilt correction algorithm using color image segmentation method, the maximum removal of image noise designed based on a variety of colored background neural network method for license plate recognition, and achieved good results.)

huffman无损编码matlab程序，图像压缩常用编码方法(huffman coding)

位置指令为幅值为1.0的阶跃信号，r（k）=1.0。网络结构为1-4-1，高斯函数的参数值取Cj=[-2 -1 1 2]T ，B=[0.5 0.5 0.5 0.5]T 。 网络权值学习参数为η=0.30，α=0.05 。PID控制各参数的初RBF网络控制，被控对象为G（s）= 取采样时间为1ms，采用Z变换进行离散化，离散化后的被控对象为 y(k)=-den(2)*y（k-1）-den(3)*y(k-2)+num(2)*u(k-1)+num(3)*u(k-2) 始值为，kp=20， kd=0.3， ki=0.1。 (Position command for the step signal amplitude of 1.0, r (k) = 1.0. The network structure is 1-4-1, the Gaussian function parameters taken Cj = [-2-1 1 2] T, B = [0.5 0.5 0.5 0.5] T. Weight learning parameter η = 0.30, α = 0.05. PID control parameters at the beginning of each RBF network control, the controlled object is G (s) = take the sampling time is 1ms, using the Z transform discrete, discretized controlled object is y (k) =-den (2)* y (k-1)-den (3)* y (k-2)+num (2)* u (k-1)+num (3)* u (k-2) initial value, kp = 20, kd = 0.3, ki = 0.1.)

This program used to forecast stock price