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Outlier_Variogram

于 2007-09-13 发布 文件大小:2KB
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代码说明:

  空间数据挖掘的又一个matlab例外点检测程序(Spatial data mining another exception detection procedures matlab)

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Outlier_Variogram.m

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  • mnyz
    一杯沸水冷却,圆柱体模型,底面半径0.05m,高0.1m,周围温度20度,初始水温100度 方程是四维输运方程(常数a^2=k/(c*p),k是热传导系数0.6006焦/(米*秒*度)) 初始条件:t=0时水等于100度 边界条件:1.上下壁都是自由冷却,第三类边界条件,周围温度保持在20度(H=k/h,h取1) 2.杯壁绝热,第二类边界条件 图形显示格式,取过圆柱轴的截面温度变化将其做成动画. (A cup of boiling water for cooling, the cylinder model, the bottom radius of 0.05m, the high-0.1m, an ambient temperature of 20 degrees, the initial water temperature of 100 degrees is four-dimensional transport equation equation (constant a ^ 2 = k/(c* p), k is the thermal conductivity coefficient of 0.6006 J/(m* s* degrees)) initial conditions: t = 0 when the water equivalent to 100 degrees boundary conditions: 1. up and down the wall are free cooling, the third boundary condition, the ambient temperature maintained at 20 degrees (H = k/h, h check one) 2.杯壁insulation, the second boundary condition graphical display format, check-off section of cylindrical shaft temperature change to make animation.)
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    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.)
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