Ex6

说明：  关于带通带阻滤波器的设计，巴特沃思滤波器 压缩包里有： bendpass.m 为带通程序 bendblock.m 为带阻程序 SE144.wav 采用的声音文件，请放在 C:\下 bendpass.wav 通过带通滤波器之后的声音，保存在C:\ bendpass.wav bendblock.wav 通过带阻滤波器之后的声音，保存在C:\ bendblock.wav (on Bandpass Filter with the design, Butterworth filter bag is compressed : bendpass.m for band-pass procedures for bandstop bendblock.m procedures SE144.wav the voice file, on the C : \ under bendpass.wav through bandpass filter after the voice preserved in C : \ bendpass.wav bendblock.wav band stop filter through after the voice preserved in C : \ bendblock.wav)

例程：通信系统与 MATLAB (Proakis)，很有益处，不妨(routines : communication system with MATLAB (Proakis), useful, to take a look)

learning about less than 1000

Normalized LMS adaptive filtering algorithm.

zoomig and add-subtract and multiplying image

BP算法的实现，压缩感知l1算法，谢谢了(BP algorithm, the compressed sensing algorithm l1, thank you)

不错的GM_EM代码。用于聚类分析等方面。( GM_EM- fit a Gaussian mixture model to N points located in n-dimensional space. Note: This function requires the Statistical Toolbox and, if you wish to plot (for k = 2), the function error_ellipse Elementary usage: GM_EM(X,k)- fit a GMM to X, where X is N x n and k is the number of clusters. Algorithm follows steps outlined in Bishop (2009) Pattern Recognition and Machine Learning , Chapter 9. Additional inputs: bn_noise- allow for uniform background noise term ( T or F , default T ). If T , relevant classification uses the (k+1)th cluster reps- number of repetitions with different initial conditions (default = 10). Note: only the best fit (in a likelihood sense) is returned. max_iters- maximum iteration number for EM algorithm (default = 100) tol- tolerance value (default = 0.01) Outputs idx- classification/labelling of data in X mu- GM centres)

transformation of coordiantes using finite diff method

Training Artificial Neural Network. XOR Problem. Summation Units, Log-Sigmoid Neurons with Biases. Input Layer: 2, Hidden Layer: 2, Output Layer: 1 neurons. Returns mean square error between desired and actual outputs. Reference Papers: D. Karaboga, B. Basturk Akay, C. Ozturk, Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed-Forward Neural Networks, LNCS: Modeling Decisions for Artificial Intelligence, 4617/2007, 318-329, 2007. D. Karaboga, C. Ozturk, Neural Networks Training by Artificial Bee Colony Algorithm on Pattern Classification, Neural Network World, 19(3), 279-292, 2009. */

基于matlab的三维时域有限差分程序（cpml吸收边界），使学习时域有限差分方法的好资料。(Matlab-based three-dimensional finite difference time domain procedures (cpml absorbing boundary), so that learning FDTD good information.)