LMS

芬兰人海韦里恩 的基于负熵最大的固定点ICA 文件很长 有详细程序说明 很有参考价值(Finn Haiweilien largest negative entropy-based fixed-point ICA file for a long detailed description of the procedures was useful)

利用网络实现函数逼近 使用的主要函数如下： NEWRB——设计径向基函数神经网络 SIM——对径向基函数网络进行仿真(Function approximation using the Internet to achieve the main function is as follows: NEWRB- the design of radial basis function neural network SIM- for radial basis function network simulation)

基于信息熵的人工免疫算法在tsp问题中的应用，求解50个城市的实现(Entropy-based artificial immune algorithm tsp Problem solving the implementation of 50 cities)

FIR语音滤波的MATLAB 程序，附带噪声的音频，和消噪后的音频，(MATLAB FIR filtering process voice, with the noise of the audio, and denoising of audio,)

《MATLAB 神经网络30个案例分析》程序和数据 包含了基本包含了神经网络的各类常见案列，并提供了具体数据用于Matlab程序计算，非常易于学习者上手和练习。( MATLAB neural network 30 case studies programs and data Contains the basic neural network contains common types of cases listed, and provides specific data for the calculation of Matlab program, very easy to use and exercise the learner.)

LMS identification volterra order 4,degree 4

用wavedec2()函数对b1,b2两幅图像进行小波分解, 采用’haar’小波, 尝试着分解到第二或第三级 融合两幅图像。(With wavedec2 () function b1, b2 two images using wavelet decomposition, using haar wavelet, trying to break into the second or third grade fusion of two images.)

Matlab的HARQ仿真及其组合译码方案(Its combination of Matlab simulation HARQ decoding scheme)

mallat快速算法实验报告 及实现算法(mallat fast algorithm and the algorithm reports experiment)

最新的支持向量机工具箱，有了它会很方便 1. Find time to write a proper list of things to do! 2. Documentation. 3. Support Vector Regression. 4. Automated model selection. REFERENCES ========== [1] V.N. Vapnik, "The Nature of Statistical Learning Theory", Springer-Verlag, New York, ISBN 0-387-94559-8, 1995. [2] J. C. Platt, "Fast training of support vector machines using sequential minimal optimization", in Advances in Kernel Methods - Support Vector Learning, (Eds) B. Scholkopf, C. Burges, and A. J. Smola, MIT Press, Cambridge, Massachusetts, chapter 12, pp 185-208, 1999. [3] T. Joachims, "Estimating the Generalization Performance of a SVM Efficiently", LS-8 Report 25, Universitat Dortmund, Fachbereich Informatik, 1999. (The newest work tools of svm,it will be very convenient to have it.)