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A code for locating an earthquake

说明：  经典的信号稀疏分解算法MP的实现。随着迭代次数增大，恢复的信号越精确。(The classic signal sparse decomposition algorithm MP . With the number of iterations increases, the more accurate the signal is recovered)

说明：  使用HOPfield神经网络求解参数线性约束规划，此代码为原创，可直接运行。(Use HOPfield Neural networks for solving parametric linear constrained programming, the code for the original, can be directly run.)

详细的介绍了matlab信号和图像处理的方法和原理(Detailed description of the matlab signal and image processing methods and principles)

说明：  fm的调制与解调程序，适合初涉软件无线电者参考和学习(fm modulation and demodulation process, suitable for software radio初涉reference and learning)

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.)

可以利用此书来编辑抗震问题的解决过程中利用matlab来实现(this is helpful to solve some problem which comes to anti-sesimic)

该程序对信号进行时域分析，包括求有效值，画时域图形等(The program is the signal time-domain analysis, including seeking the valid values ​ ​ for paintings domain graphics)

Two dimensional FDTD with UPML + TFSF

一个简单的优化案例。求解下面的带有等式约束和简单的边框约束(box constraints)的优化问题： minx,y(x?1)2+(y?2)2s.t.0≤x≤3,1≤y≤4,2x+3y=5(A simple optimization case. To solve the following with equality constraints and simple edge constraint optimization problem (box constraints): Minx, y (x - 1) 2+ (Y - 2) 2s.t.0 = x = 3,1 = y = 4,2x+3y=5)