MATLAB_2ASK

matlab程序，生成小世界网络，可输入概率P，生成小世界网络模型(WS small world network)

研究表明超高斯分布更加贴近语音信号的实际分布，然而语音信号很难用单一的概率密度 函数准确描述，针对这一情况，提出了一种用超高斯混合模型对语音信号幅度谱建模的新方法，并推导了 基于此模型的幅度谱最小均方误差估的估计式。仿真结果表明：与传统的短时谱估计算法相比，该算法不 仅能够进一步提高增强语音的信噪比，而且可以有效减小增强语音的失真度，提高增强语音的主观感知 质量。 (Recent research indicates that the speech spectral amplitude distributions could be fairly described with super-Gaussian probability density function. However, the complexity of speech signal determines that the distribution statistics ofspeech signal could not be well described by single simple function. Thus a super-Gaussian mixture model for speech spectral amplitude is proposed, and with this model, a minimum mean-square error (MMSE) estimator for speech signals spectral amplitude is derived. The simulation results show that this algorithm based on Gaussian and super-Gaussian speech model could achieve better noise suppression and lower speech distortion as compared with the conventional short-time spectral amplitude estimation algorithm. )

这是一本关于模糊控制在matlab环境中应用的书，对于现代控制的实现有很好的帮助作用。 (This is the one on the Application of Fuzzy Control in matlab environment for the book, for the realization of modern control has very good help.)

Graphical wpf programmation langage C#

一本matlab程序设计的图书，可以作为研究人员参考，作者刘卫国(A matlab program designed books, you can reference as researchers of Liu Weiguo)

堆栈自编码，通过两个稀疏自编码的堆叠和softmax分类模型，实现手写体的分类。(Stack self-encoding, since encoding by two sparse stack and softmax classification model to classify handwriting.)

仿真信号的FFT频谱和功率谱，绘制了emd分解结果，以及瞬时包络和瞬时频率图，还有Hilbert-Huang谱和Hilbert边际谱。(The FFT spectrum and power spectrum of the simulated signal, the emd decomposition results, and the instantaneous envelope and instantaneous frequency maps, as well as the Hilbert-Huang spectrum and the Hilbert marginal spectrum.)

Matlab中Simulink建模与仿真实践课件，初学者的入门宝典(Matlab Simulink modeling and simulation in practice courseware, introductory book for beginners)

1、GP_Algorithm_main 联合开发网 - pudn.com

Fit time series Q(t) to a logistic function. Inputs (vectors of same size): t (time) & Q Outputs: Qpre (logistic model fit to data) and three independent parameters: thalf, Qinf, and a, describing the logistic Q(t) = Qinf/(1 + exp(-a*(t-thalf))) Qinf is value as t --> infinity thalf is time of symmetric inflection point a is time decay constant Written by James Conder, Southern Illinois University, Oct. 2010