Butterworth_Filters_v1

各算法对应的问题如下： 　　PSO 用基本粒子群算法求解无约束优化问题 　　YSPSO 用带压缩因子的粒子群算法求解无约束优化问题 　　LinWPSO 用线性递减权重粒子群优化算法求解无约束优化问题 　　SAPSO 用自适应权重粒子群优化算法求解无约束优化问题 　　RandWPSO 用随机权重粒子群优化算法求解无约束优化问题 　　LnCPSO 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 　　AsyLnCPSO 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 　　SecPSO 用二阶粒子群优化算法求解无约束优化问题 　　SecVibratPSO 用二阶振荡粒子群优化算法求解无约束优化问题 　　CLSPSO 用混沌粒子群优化算法求解无约束优化问题 　　SelPSO 用基于选择的粒子群优化算法求解无约束优化问 　　BreedPSO 用基于交叉遗传的粒子群优化算法求解无约束优化问 　　SimuAPSO 用基于模拟退火的粒子群优化算法求解无约束优化问题(13 PSO algorithms and coursewares)

光通信中抑制载波型差分相位调制信号建模与仿真分析(Optical Communication inhibit carrier-based differential phase modulation signal modeling and simulation analysis)

说明：  这是一个利用perl调用matlab的接口函数，可以在perl环境下运行matlab函数(This is a call using perl interface matlab function, you can run perl environment matlab function)

小世界模型 近日需要用到无标度网络与小世界网络，早上用matlab写了这两种网络的matlab程序，放在这，有用者可以拿去用，请自己验证正确性后使用。 (Small-world model)

关于QPSK信号经过莱斯信道误码性能分析，有莱斯信道解释及matlab源代码(QPSK signal on the Rician channel error performance analysis, there are Rician channel interpretation and matlab source code)

matlab code for jammer modelisation

AHP toolbox AHPCALC Perform a complete AHP calculation. AHPHIER One-step hierarchical assembly of AHP weighting vector. AHPREORDER Internal reordering function called by AHPCALC. AHPVECTOR Calculate the AHP ranking or weighting vector from an AHP reciproal matrix. AHPMAKEMAT Turn a vector of comparisons into an AHP reciprocal matrix. Copyright (C) 2001 Michael J. Scott

pear1.5的安装包，解压到smarty的目录基本就可以使用了。(pear1.5 installation kits, smarty extract the basic directory can use it.)

基于谱聚类的新数据聚类算法，可用于一般的时间序列数据聚类及图像分割方面的应用，还包含相应的文章和算法说明。(Executes the spectral clustering algorithm defined by type on the adjacency matrix W and returns the k cluster indicator vectors as columns in C.)

二次规划师非线性优化中的一种特殊情形，它的目标函数是二次实函数，约束函数都是线性函数。由于二次规划比较简单，便于求解（仅次于线性规划），并且一些非线性优化问题可以转化为求解一些列的二次规划问题，因此二次规划的求解方法较早引起人们的重视，称为求解非线性优化的一个重要途径。二次规划的算法较多，本文仅介绍求解等式约束凸二尺规划的拉格朗日方法以及求解一般约束凸二次规划的有效集方法。(Quadratic nonlinear optimization planners in a special case, its real objective function is a quadratic function, constraint functions are linear functions. Because of quadratic programming is relatively simple and easy to solve (after linear programming), and some non-linear optimization problem can be transformed into solving quadratic programming problems some columns, solving quadratic programming method therefore attracted attention earlier, saying an important way for solving nonlinear optimization. More quadratic programming algorithm, this paper describes only two feet Solving the Equality Constrained Convex Programming effective Lagrangian method and set method for solving general constrained convex quadratic programming.)