feijimoni

这是有个基于matlab的单一感知器神经元应用的实例(This is a matlab-based single perceptron neuron Examples of applications)

OFEM接收机的解调，检测合格，效果良好，本人亲测(the demodulation of OFDM receiver)

本软件能实现复杂网络分析，功能强大,能计算复杂网路的度，簇系数，聚类和社区发现。(This tool can finish analysing complex networks, the function is complete.)

有关LDPC的一些资料和英文文献以及一些相关的资料(LDPC)

Linear Prediction using Levinson Durbin Recursion and Lattice Filters

设计特定的进化操作或约束修正因子。文献[ 28 ]采用罚函数法,利用非固定多段映射罚函数对约束优化问题进行转化,再利用PSO求解转化后的问题,仿真结果显示PSO相对进化策略和遗传算法有优越性,但其罚函数的设计过于复杂,不利于求解(Operation or design of the evolution of specific binding correction factor. Literature [28] The penalty function method, using non-stationary multi-stage fine mapping function to transform constrained optimization problem, and then transformed using PSO to solve the problem, simulation results show that PSO is relatively evolution strategy and genetic algorithm has advantages, but the penalty function The design is too complex and not conducive to solving)

基于MATLAB的近似熵程序,主要用与生物信号的检测,主要用于生物医疗工程的信号处理方面(matlab program for calculating approximate entropy in biomedical signal processing especially.)

Short Fourier Transform for a non periodical signal

说明：  粗糙集的属性约简 离散化，规则提取的方法有说明(rough set reduce)

特徵粹取（feature extraction）是特徵選取（feature selection）的延伸，簡單地說，我們希望將資料群由高維度的空間中投影到低維度的空間，因此，我們必須找出一組基底向量（base）來進行線性座標轉換，使得轉換後的座標，能夠符合某一些特性。 我們可以將特徵粹取分成「包含類別資訊」和「不包含類別資訊」兩大類。包含類別資訊指的是我們已經知道哪些資料分別歸屬於哪一類；而不包含類別資訊的特徵粹取則適用於我們不知道手上的資料點分別該歸屬於哪一類，甚至連該劃分成幾類都不知道的情況。對於這兩大類資料，可以分述如下： 對於「不包含類別資訊」的資料，我們通常使用「主要分量分析」（principal component analysis），簡稱 PCA。 (Cuiqu features (feature extraction) is feature selection (feature selection) extension, simply put, we want to base the projection data the high-dimensional space to low-dimensional space, so we have to find a set of basis vectors ( base) to the linear coordinate conversion, so that after the coordinate conversion, can meet a number of features. We can feature Cuiqu into " category contains information" and " does not contain a category of information," two categories. Contains categories of information refers to what we already know what kind of information are attributed to Cuiqu features include categories of information are not applicable to information we do not know the points are in the hands of the home in which category, and even that is divided into several class do not know the situation. For these two categories of information, can be divided as follows: For information " does not include the categories of information" , we usually use the " )