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yinlingling7839木虫 (小有名气)
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Investigations may fail because the classic approach is to modify one variable at a time, the OVAT approach . Unfortunately many processes are complex, and the OVAT approach may cause investigators to ignore potential optimizations from inter-dependent variables. A more organized approach, employing statistical design of experiments (DOE), may be necessary to optimize conditions involving many parameters . Statistical approaches are usually undertaken to optimize reactions that do not readily yield to intuition-based optimizations or to optimize manufacturing operations where small changes in yields or productivity can have substantial impact on manufacturing costs. Some of the approaches included in DOE include factorial design, simplex optimization (self-directed optimization), and response surfaces . In factorial design, two (or more) values are assigned to key variables, and experiments are selected at random and run to determine the optimal results. Thus for a system comprising five variables at two settings each, the total number of runs would be 25, or 32. Usually 2n-1(2的n-1次方) runs will allow effective optimization from only half the number of runs, 16 in this case . If a variable can be eliminated because it probably has little impact, the number of experiments can be reduced further to 2n-2, or only eight runs for initial optimization. Computer programs are available to assist in DOE optimization [ Last edited by yinlingling7839 on 2010-1-16 at 16:37 ] |
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yinlingling7839(金币+20,VIP+0): 1-17 11:47
yinlingling7839(金币+20,VIP+0): 1-17 11:47
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Investigations may fail because the classic approach is to modify one variable at a time, the OVAT approach . 投资也许是失败的，因为如OVAT此类经典的方法只能在一个时间里修正一个变量。Unfortunately many processes are complex, and the OVAT approach may cause investigators to ignore potential optimizations from inter-dependent variables. 不幸的是，许多的（投资）过程是非常复杂的，而OVAT方法却可能引起投资者忽略那些来自于相互独立变量的潜在的选择。 A more organized approach, employing statistical design of experiments (DOE), may be necessary to optimize conditions involving many parameters . 一个较好构成的方法(雇佣统计试验设计)也许对于包含许多参量的条件优化是很有必要的。 Statistical approaches are usually undertaken to optimize reactions that do not readily yield to intuition-based optimizations or to optimize manufacturing operations where small changes in yields or productivity can have substantial impact on manufacturing costs. 统计方法常用来优化那些基于直觉选择所易于获得的反应，或者用于优化制造业操作在产生的结果中有小的改变的场合，或者在生产率对于制造成本具有较大影响的场合。 Some of the approaches included in DOE include factorial design, simplex optimization (self-directed optimization), and response surfaces . 包括DOE等的一些方法包涵着工业设计、简单优化（自指示优化）以及响应表面等。 In factorial design, two (or more) values are assigned to key variables, and experiments are selected at random and run to determine the optimal results. 在工业设计中，两个或更多的值被设计用于关键的变量，并且要随机选择试验且进行试验以便确定优化结果。 Thus for a system comprising five variables at two settings each, the total number of runs would be 25, or 32. Usually 2n-1(2的n-1次方) runs will allow effective optimization from only half the number of runs, 16 in this case . 如此一来，一个系统包涵在2种设计中有5个变量，全部的运行的参量可能是25或者32个。一般2的n-1次方次运行之后，可以有效地优化运行的一半的参量。在32个参量的情况下，可优化的是16个。 If a variable can be eliminated because it probably has little impact, the number of experiments can be reduced further to 2n-2, or only eight runs for initial optimization. 如果一个参量的影响较小的话，我们可以忽略其影响，这样一来，实验可以减少至2n-2次，或者在最初的优化中我们仅需要运行8次。 Computer programs are available to assist in DOE optimization 在DOE优化中，计算机程序可以作为一个辅助的手段。 |
2楼2010-01-16 18:00:37