ADwienerfilt

采用神经网络控制的方法。利用人工神经网络的自学习这一特性，并结合传统的PID控制理论，构造神经网络PID控制器，实现控制器参数的自动调整(Control method using neural network. Artificial neural network to learn the characteristics of the self, combined with the traditional PID control theory, structural neural network PID controller, automatically adjust the controller parameters )

这是一个贝叶斯独立分量分析(ICA)算法的线性瞬时混合高斯噪声模型和添加剂。解决问题的是ML-II推论,即资源的整合在发现源后和噪声协方差矩阵和混合了最大化的边际似然。充分统计量的估计平均场或变分理论和线性响应修正或通过自适应平均场理论水龙头。平均场方程,解决了信仰传播法的或连续的迭代。(This is a bayesian independent component analysis (ICA) algorithm of instantaneous linear mixed gaussian noise model and additives. The solution to the problem of is ML-II inference, namely of the integration of resources and noise source in found that after the covariance matrices and mixed the maximization of marginal likelihood. Sufficient statistics estimate of the mean field or variational theory and linear response correction or through the adaptive average field theory faucet. Average field equations are solved belief propagation method or continuous iteration.)

各种干扰信号,包括噪声调频干扰,噪声调相干扰,噪声调幅干扰,射频噪声干扰(A variety of interfering signals, including noise FM interference, noise, phase modulation, interference, noise AM interference, RF noise)

pid tuning by using PSO algorithm

本代码为基于matlab的小波功能实现一书中9到19章节中的代码，包括术中各例题的代码。(The code for matlab-based wavelet function of a book of the 9-19 section of the code, including the code of the examples in the surgery.)

西储大学电机轴承故障诊断，EMD分解程序，含包络谱，能量谱(Case western reserve university motor bearing fault diagnosis, the EMD decomposition procedure, including envelope spectrum, energy spectrum)

说明：  数字图像融合的程序，包括加权平均.HIS和和PCA等算法(Digital image fusion procedures, including the weighted average. HIS and other algorithms and PCA)

用matlab语言编写的遗传算法工具箱，作为一种新的全局优化搜索算法,遗传算法以其简单通用、鲁棒性强、适于并行处理... (using Matlab language of the genetic algorithm toolbox, as a new search algorithm for global optimization, Genetic Algorithm with a simple, common, robust and suitable for parallel processing ...)

p pi and pid control which is very important to control strategy .

Finally, probability analysis, based on the chosen database, is performed and judgment whether the person is recognized is pronounced. Two different modeling techniques can be suggested for purposes of person identification or verification. The approach based on Hidden Markov Speaker Models (HMMs) with the mixtures of Gaussian distribution is text-dependent one. For this technology a speaker is described by a set of HMMs, constructing the set of speech groups spoken by that person. The speech groups may be phonemes or words [7]. The text-independent technology is rested upon Gaussian Mixture Speaker Models. For this technology each speaker is defined by only one model describing all utterances of that person. Identification in the range of both approaches is executed by evaluation of maximum a posteriori probability [7].