| 成龙 |
2009-09-15 14:42 |
How Many Rays Do I Need for Monte Carlo Optimization? ,&qC
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While it is important to ensure that a sufficient number of rays are traced to 7GWOJ^)
distinguish the merit function value from the noise floor, it is often not necessary to ~ BX=n9
trace as many rays during optimization as you might to obtain a given level of RtzS e$O
accuracy for analysis purposes. What matters during optimization is that the 2E[7RBFY+\
changes the optimizer makes to the model affect the merit function in the same way >]z^. U7=
that the overall performance is affected. It is possible to define the merit function so l {>j8Ln
that it has less accuracy and/or coarser mesh resolution than meshes used for $Ery&rX.
analysis and yet produce improvements during optimization, especially in the early ?Ve I lD
stages of a design. W)/^*,
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A rule of thumb for the first Monte Carlo run on a system is to have an average of at tiHR&v
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays d]" 4aS
on the receiver to achieve uniform distribution. It is likely that you will need to oPM*VTMA
define more rays than 800 in a simulation in order to get 800 rays on the receiver. fe,6YXUf
When using simplified meshes as merit functions, you should check the before and pDSNI2
after performance of a design to verify that the changes correlate to the changes of i i- AE L
the merit function during optimization. As a design reaches its final performance j)6p>6
level, you will have to add rays to the simulation to reduce the noise floor so that #mA(x@:*
sufficient accuracy and mesh resolution are available for the optimizer to find the F_jHi0A
best solution.
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