yundongxuezhengjie

信息论学习过程中经常用到的一些编码、信道容量等问题(Information theory are often used in the learning process some coding, channel capacity and other issues)

数值分析matlab代码，Gauss迭代，Jacobi迭代，SOR迭代，Newton迭代，符合Simpson，最佳平方逼近等(Numerical Analysis Code of Gauss-Seidel,Jacobi,SOR,Newton,Simpson)

[FFT]_matlab中关于FFT的使用(理解频率分辨率、补零问题).( [FFT ] _ matlab on the FFT use (understanding the frequency resolution, zero problem ). )

实现matlab中柱状图的绘制，解决论文排版中黑白需求(Matlab realize the histogram drawing)

这是一个电力系统传输线PI型matlab/simulink仿真实例(This is a power system transmission line PI type matlab/simulink simulation examples)

NP是美国匹兹堡大学的T.L.Saaty 教授于1996年提出了一种适应非独立的递阶层次结构的决策方法，它是在网络分析法（AHP）基础上发展而形成的一种新的实用决策方法。其关键步骤有以下几个： 1 确定因素，并建立网络层和控制层模型。 2 创建比较矩阵。 3 按照指标类型针对每列进行规范化。 4 求出每个比较矩阵的最大特征值和对应的特征向量。 5 一致性检验。如果不满足，则调整相应的比较矩阵中的元素。 6 将各个特征向量单位化(归一化)，组成判断矩阵。 7 将控制层的判断矩阵和网络层的判断矩阵相乘，得到加权超矩阵。 8 将加权超矩阵单位化(归一化)，求其K次幂收敛时的矩阵。其中第j列就是网络层中各元素对于元素j的极限排序向量。 (NP is a professor at the University of Pittsburgh TLSaaty presented in 1996, an adaptation of non-independent Hierarchy of decision-making method, which is the analytic network process (AHP) formed on the basis of the development of a new and practical decision-making method . The key steps are the following: A determining factor, and a network layer and control layer model. 2 create a comparison matrix. For each of the three types of indicators in accordance with normalized columns. 4 find the maximum for each comparison matrix eigenvalue and the corresponding eigenvectors. 5 consistency test. If not satisfied, then the comparison to adjust the corresponding matrix elements. 6 will each feature vector units of (normalized), to determine the composition of matrix. 7 to determine the control layer and network layer to determine matrix matrix multiplication, to be weighted super-matrix. 8 of the weighted super-matrix units of (normalized), seeking the powe)

说明：  MLS论文 关于MLs的论文 有推导过程 可以参考(MLS thesis paper on the MLs can refer to a derivation)

说明：  语音信号的LPC参数提取及合成，可以提取基音及残差信号(LPC parameters of speech signal extraction and synthesis, and the residual signal can be extracted pitch)

说明：  神经网络与免疫算法的结合，用matlab编程实现小功能(Neural network with a combination of immune algorithm with a small functional programming matlab)

该方法为遗传算法，能解决一系列不定问题，所以效果很好。(The method of genetic algorithm, can solve a series of indefinite problems, so very good.)