CODEProgram25_FDTD_2D_with_PMC_boundary

说明：  神经网络实例集。包括以下几个程序单层线性神经网络实例、感知器神经元解决较复杂输入向量的分类问题、基于感知器神经网络处理复杂的分类问题、数值分析程序matlab-GUI、用BP网络完成函数的逼近源程序、自组织特征映射应用实例(Examples of neural network sets. Procedures include the following examples of single-layer linear neural network, perceptron neuron input vector to solve more complex classification problems, based on the perceptron neural network to deal with complex classification problems, numerical analysis matlab-GUI, using BP network function source approximation, self-organizing feature map application)

该函数由于遗传算法，粒子群算法等各类测试，可以很好地对你所编程序进行评估，以验证你提出的算法的合理性(Function due to genetic algorithms, particle swarm optimization, and other tests can be well on your programmed assessment, to verify the rationality of the proposed algorithm)

功率谱密度的几种新求法,有着各种各样不同的前所未有的方法，都很有有点(some solution of fft)

利用matlab语言实现常微分方程的求解，可以很好的解决常微分方程问题(Matlab language to achieve the solution of ordinary differential equations, can solve the problems of ordinary differential equations)

用MATLAB内含的随机数生成器生成所需的随机量，是进行概率模拟编程的参考工具(Included with the MATLAB random number generator to generate the required random quantity is the probability of a reference tool for simulation programming)

Matlab filter conversion

说明：  公共交通最佳路径算法在分析城市道路网络最短路径算法 SP算法 和公交网络的特点的基础上,提出公共交通系 ( ) ( ) 统最佳路径算法.首先引入直达矩阵 T矩阵 和最小换乘矩阵 Q 矩阵 ,讨论公交网络节点间换乘 问题,得出最少换乘算法.利用 Q 矩阵确定节点间最少换乘次数,评价公交网络方便可达性.其次 ( ) 结合最少换乘算法,对最短路径算法 Dijkstra 算法 进行改进.在标号过程中,利用 Q 矩阵对待检验 T标号点进行筛选,减少 T 标号计算量,得到一条综合考虑路径长度和换乘的最佳路径.最后用一 个简单的算例进行验算,说明该算法适用于一般公交网络,特别是换乘代价较高的公交网络.(Public transport system the best path algorithm )

说明：  捷联惯导 四元数法 进行导航姿态解算 程序内有详细注解(SINS quaternion attitude solver for navigation with detailed comments)

一篇基于Matlab的Gauss_Seidel迭代法电力系统潮流计算的文献(Gauss-Seidel Iteration Method in Flow Calculation Based on Matlab)

用 LDPC 解码法，对原始信号通道的噪声进行评估，消去。具体原理可参照IEEE TCOM的论文。(Channel Noise Estimation or correlation estimation for distributed source coding Using Particle based Belief Propagation for LDPC decoding in AWGN and BSC models. This code is partially based on our published journal papers with some additional improvements. In this code, we removed the parameter lambda to reduce the number of free parameters. PBP estimator is distributed in the hope that is will be useful, but without any warranty. The codes have not been optimized, so the estimation speed may be slow. To use PBP estimator source code in your research, please cite our journal papers published in IEEE TCOM. )