shuzixinhaochuli_daan

普通的通信系统仿真平台的搭建，从发射端到接收端。(Ordinary communication system simulation platform structures, from the launch-ended receiver.)

高阶谱分析中的实现三阶累积量计算的源程序。(High-order spectral analysis of the calculation to achieve third-order cumulants of the source.)

一种关于椭圆球面波函数脉冲的近似数值解法(a novel uwb pulse design algorithm)

自编四参数法生成多孔介质的matlab程序代码(Four-parameter method to generate self matlab code porous media)

48阶滤波器，包括两个matlab文件与滤波器的详细介绍与实现(48-order filter, including the two documents and filter matlab detailed introduction and implementation of)

包括：用基本粒子群算法求解无约束优化问题，用带压缩因子的粒子群算法求解无约束优化问题，用线性递减权重粒子群优化算法求解无约束优化问题，用自适应权重粒子群优化算法求解无约束优化问题，用随机权重粒子群优化算法求解无约束优化问题，用学习因子同步变化的粒子群优化算法求解无约束优化问题，用学习因子异步变化的粒子群优化算法求解无约束优化问题，用二阶粒子群优化算法求解无约束优化问题，用二阶振荡粒子群优化算法求解无约束优化问题(Include: using the particle swarm algorithm for unconstrained optimization problems, with compression factor of particle swarm algorithm for solving unconstrained optimization problem with linear decreasing weight particle swarm optimization for unconstrained optimization problems, an adaptive weight particle swarm optimization unconstrained optimization problems, random weight particle swarm optimization algorithm for unconstrained optimization problems, with the learning factor synchronous change of particle swarm optimization algorithm for unconstrained optimization problems, with the learning factor asynchronous changes in particle swarm optimization algorithm for solving unconstrained optimization problem , with the second particle swarm optimization for unconstrained optimization problems, with the second-order oscillating particle swarm optimization algorithm for unconstrained optimization problem)

dipole 天线仿真dipole 天线周围场的分布(dipole antenna simulation)

MATLABA初级教程，可供各位编程爱好者查询使用，内有大部分MATLAB常用函数以及使用方法和技巧(MATLABA junior tutorial, for you to use programming enthusiasts query, there are most commonly used MATLAB functions and the use of methods and techniques)

MATLAB环境下可以产生PN随机序列的仿真程序(the simulation program of generating PN random sequences )

Gist。 使用直方图的方式提取特征值里面有demo，修改一下可以直接使用，提取特征向量，使用机器学的方法来分类识别( Use the histogram to extract eigenvalues ​ ​ inside demo, change it can be directly used to extract feature vectors, using machine learning approach to classification)