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MATLAB经典示例 以示参考 十分有用(MATLAB classic example to show reference useful)

在matlab中实现模式识别中的一个分类器——fisher分类器(Pattern recognition in matlab to implement a classifier- fisher classifier)

实现OOK的matlab程序，是递归最小二乘法的推广和运用

针对粒子群优化算法应用于约束优化问题时易陷入局部极小值的问题, 提出了一种改进的粒子群优化算 法. 该算法综合了约束优化问题的目标函数值和约束函数的违反度值作为粒子群优化算法的双适应度值, 采用了 双适应值动态判断粒子群优化算法中粒子的优劣. 违反度值的计算引入了自适应加权系数, 相应地提出了调整各 权系数的自适应策略, 并改进了粒子群优化算法的粒子竞争选择策略, 拓展了粒子群优化算法的单适应值的应用 范围.应用约束自适应粒子群优化算法实现了城市水厂的节能优化调度. 结果表明, 该算法收敛速度快且结果可 靠. 粒子群优化算法为解决工程约束优化问题提供了一条可行途径(Considering that theparticleswarmoptimization( PSO) algorithmcanbeeasily trappedinto the local minimal valueinconstrainedoptimizationproblems, amodifiedconstrainedparticleswarmoptimiza
tionalgorithmwasproposed. Theobjective functionvalue andthe violationvalue of constraint functions wereeffectively combinedto formtwofitnesses, andthefitnesseswereadoptedto estimate if theparticle wassuperior or not ina dynamicway. Theadaptiveweight functionwasadoptedinthe calculationof the violationvalue. The strategy of keeping anadaptive relationof weight coefficientswasproposed, andthe strategyof swarmtournament selectionwasimproved. Theapplicationlocalizationsof thesinglefitnessof PSOwerewidenedaswell. ThemodifiedconstrainedPSOalgorithmwasappliedtosolveenergyoptimiza
tionproblemsof theurbanwater supplyprocess, whichshowedthat theconvergent speedof thealgorithm isfast andthe result isvalid. Afeasibleapproachto solvetheindustrial constraint optimizationproblems withPSOwasprovided.)

自学matlab 的60个好程序，帮助你掌握Matlab 的基本是使用格式和命令

对单炮地震数据抽稀：地球物理物探工作者必备程序(a procedure of vacuate of signal shot data)

基于李雅普诺夫稳定性分析的模型参考自适应系统仿真(Based on the lyapunov stability analysis model reference adaptive system simulation based on lyapunov stability analysis model reference adaptive system simulation )

image compression algorithm

采用非线性动力学分析方法来研究互联网拥塞控制系统中存在 的复杂非线性动力学现象，如分岔(bifurcation)、混沌(chaos)等，针对不同参数该组源程序仿真互联网拥塞控制系统频闪模型的非线性动力学现象。(The programs study the dynamic behavior in stroboscopic model of Internet congestion control system is studied. )

Dsp Course lec 11 DSP course