Maximum Mapping Errors

  • Edward Layer


The success of any simulation experiment depends on the accuracy of the model upon which the experiment is performed. This accuracy is verified by the determination of model errors that are defined in different function spaces, by means of chosen error functionals. The determination of errors is possible on the precondition resulting from a previous assumption of a determined input signal used for computations. Traditionally, the dynamic errors are computed for standard input signals, most often in the form of the unit step function, Dirac’s impulse or less often of ramp or sinosoidal inputs. There are well established and commonly used methods of computing the various error criteria for these signals, among which the most popular are the integral-square-error and absolute error criterions. As a result, different error values are obtained, since they depend essentially on the input signal for which they are computed. This is a significant limitation of their usefulness, because in practice real systems are not excited by standard signals, but usually by unknown dynamic signals, which are decidedly different from the standard ones. For that reason, the determination of such errors, whose values will be valid for arbitrary dynamic signals, which can appear at the input of the investigated system, is of essential importance. It should be stressed that these can be non-determined signals whose form we are not able to predict a priori. It can be noted that the solution of a problem posed in such a way, which could make the error values independent of the input signal form, is possible for maximum errors. However the procedure of determination of maximum errors requires special input signals to be used, which warrant that the error values determined with them will always be higher or at least equal to the value generated by any other signal.


Input Signal Impulse Response Mapping Error Maximum Error Maximum Magnitude 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Edward Layer
    • 1
  1. 1.Faculty of Electrical and Computer Engineering Institute of Electrical MetrologyCracow University of TechnologyKrakowPoland

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