Difference between revisions of "Tips"

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* By default Matlab only allocates about 117 MB memory space for the Java Virtual Machine. If you would like to increase this limit (which you should) please follow the instructions [http://www.mathworks.com/support/solutions/data/1-18I2C.html?solution=1-18I2C here]. See also the general memory instructions [http://www.mathworks.com/support/tech-notes/1100/1106.html here].
 
* By default Matlab only allocates about 117 MB memory space for the Java Virtual Machine. If you would like to increase this limit (which you should) please follow the instructions [http://www.mathworks.com/support/solutions/data/1-18I2C.html?solution=1-18I2C here]. See also the general memory instructions [http://www.mathworks.com/support/tech-notes/1100/1106.html here].
   
* You can switch off adaptive sample selection if you do not specify a <SampleSelector> tag. See [[Adaptive Modeling Mode]].
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* You can switch off adaptive sample selection if you do not specify a [[SampleSelector| <SampleSelector>]] tag. See [[Adaptive Modeling Mode]].
   
 
* Remember that the Measure (and error function you use) strongly influence the quality and fit of the surrogate model. If you are unhappy with the final model, try a different [[Measures| Measure]] and/or [[FAQ#How_do_I_change_the_error_function_.28relative_error.2C_RMS.2C_....29.3F| error function]].
 
* Remember that the Measure (and error function you use) strongly influence the quality and fit of the surrogate model. If you are unhappy with the final model, try a different [[Measures| Measure]] and/or [[FAQ#How_do_I_change_the_error_function_.28relative_error.2C_RMS.2C_....29.3F| error function]].

Revision as of 12:21, 6 June 2008

  • If you want to benchmark your computer for Matlab speed simply run "bench" in matlab
  • By default Matlab only allocates about 117 MB memory space for the Java Virtual Machine. If you would like to increase this limit (which you should) please follow the instructions here. See also the general memory instructions here.
  • Remember that the Measure (and error function you use) strongly influence the quality and fit of the surrogate model. If you are unhappy with the final model, try a different Measure and/or error function.
  • If you use the RBF neural network model type and you get a crash in "newrb" this is an error in the Matlab Neural Network Toolbox implementation and not anything we can do about (a workaround is available on request). This should be fixed by Matlab 7.5.