CSTR

通过这个算法可以确定BP神经网络中隐层最优神经元个数(Determine the BP neural network hidden layer neuron number of optimal)

说明：  此程序包包含了霍夫变换的大部分主要程序，常用于抑制噪音等(This package contains the Hough transform most of the major procedure commonly used in noise suppression)

电磁转矩电动机广泛应用于电液伺服阀。它们有助于扭矩转换成比例的控制电流。(The electromagnetic torque motors are widely used in electrohydraulic servovalves. They serve to convert the control current into a proportional torque. For more details about this issue, please refer to my book:)

A dynamic estimation of channel is necessary before the transmission of the signals since the wireless channel is time–varying..we use MMSE method for channel estimation. The MMSE method calculates the following, MMSE channel estimator is designed to minimize the estimation MSE. The MMSE estimate the channel responses.

说明：  利用matlab实现高斯伪谱法的火箭飞行轨迹模拟(Simulation of rocket flight trajectory based on Gauss pseudospectral method using MATLAB)

《MATLAB及在电子信息课程中的应用》一书中所有例题和习题的MATLAB源程序(MATLAB and electronic information in the application of the curriculum," a book of all the MATLAB examples and exercises source)

这是一个双通道完全重构滤波器设计matlab源代码(This is a dual-channel perfect reconstruction filter design matlab source code)

This file is a solution for vertical seismic profile by mont carlo algoritm

一维小波包在matlab分解六层分解，与重构，可得高低频。与原高低频对比。( One dimensional wavelet packet decomposition in the MATLAB decomposition of the six layer, and the reconstruction can be HISTEP low frequency. Contrast with the original high and low frequency. )

说明：  相关向量机（Relevance Vector Machine，简称RVM）是Micnacl E.Tipping于2000年提出的一种与SVM（Support Vector Machine）类似的稀疏概率模型，是一种新的监督学习方法。 它的训练是在贝叶斯框架下进行的，在先验参数的结构下基于主动相关决策理论（automatic relevance determination，简称ARD）来移除不相关的点，从而获得稀疏化的模型。在样本数据的迭代学习过程中，大部分参数的后验分布趋于零，与预测值无关，那些非零参数对应的点被称作相关向量（Relevance Vectors），体现了数据中最核心的特征。同支持向量机相比，相关向量机最大的优点就是极大地减少了核函数的计算量，并且也克服了所选核函数必须满足Mercer条件的缺点。(Relevance Vector Machine (RVM) is a sparse probability model similar to SVM (Support Vector Machine) proposed by Micnacl E. Tipping in 2000. It is a new supervised learning method. Its training is carried out under the Bayesian framework. Under the structure of prior parameters, it is based on Automatic Relevance Determination (ARD) to remove the irrelevant points, so as to obtain the sparse model. In the iterative learning process of sample data, the posterior distribution of most parameters tends to zero, which is independent of the predicted value. The points corresponding to non-zero parameters are called Relevance Vectors, which represent the most core features of the data. Compared with support vector machine, the biggest advantage of correlation vector machine is that it greatly reduces the computation amount of kernel function, and also overcomes the shortcoming that the selected kernel function must meet Mercer's condition.)