JGuiB for Boomer for Mac OS X free download, JGuiB for Boomer for Mac OS X download on software download

Advertisement JGuiB for Boomer for Mac OS X is a Home & Education software developed by David W. A. Bourne. After our trial and test, the software is proved to be official, secure and free. Here is the official description for JGuiB for Boomer for Mac OS X: A. Gauss-Newton This is a method based on expanding the model Equation(s) of Interest in a Taylor series . B. Damping Gauss-Newton The correction term applied to the 'old' parameter values is reduced by a Factor of two if the 'new' parameter values result in a worse value for WSS. This is allowed up to 25 times during each iteration, although rounding error may become significant above 15 reductions. C. Modified Marquardt The Marquardt method is intermediate between the Taylor series (Gauss-Newton) method and the method of steepest descent (gradient method). In the method of steepest descent the new parameter values are calculated in the direction of the negative slope of the WSS with respect to each of the parameters. Marquardt has suggested that the direction of change in parameters calculated by these two methods is not always in the same direction. Marquardt's method attempts to find a best compromise . This method as used in MULTI and MULTI-FORTE has been further modified by Flecher . D. Simplex The simplex method of Nelder and Mead is the fourth option. This method involves the progression of a moving simplex across the WSS surface. Typically the worst point of the simplex is projected across the center (centroid) of the simplex to a better value. There are a series of simple rules to move the simplex to the WSS minimum. It is a relatively robust (but slower) method . In MULTI (line 3500) one of the rules was altered slightly. This rule was changed back to the Nelder-Mead version in MULTI-FORTE and Boomer. E. Simplex -> Damping Gauss-Newton The Simplex method is quite robust and can be used with less accurate initial parameter estimates. However, it is not possible to estimate parameter value variances by this method. Thus it may be useful to automatically proceed from the final Simplex values to the input stage of another method. Presently that other metho

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