By Olaf Wolkenhauer
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Extra resources for Data engineering : fuzzy mathematics in systems theory and data analysis
Olaf Wolkenhauer Copyright 2001 by John Wiley & Sons, Inc. ISBNs: 0-471-41656-8 (Hardback); 0-471-22434-0 (Electronic) 2 Uncertainty Techniques 0 The expectation cl Averaging cl Aggregating operator information leads to probability information cl The least-squares models. is a generic concept to summarize measures and statistics. leads to fuzzy measures and possibility criterion provides optimal cl A geometric representation of the regression optimal solution implies orthogonality. cl The Fourier series is an example El The least-squares sense.
ZOWX! by certain other changes. Considering dx/dt l,hat, the variation dx undergone during the time problem is not a syntactic but a semantic one; it t,han with the formulations and representations of = f(t) or dx = f(t)dt, it merely asserts interval dt, equals f(t)dt. The causal has to do with the interpretation rather theories. 26 SYSTEM ANALYSIS x(k + 1) UCk> ) W) ’ ’ Y(k) > delay 1 *(k Fig. 15 x(k) unit Wk) 4 + 1; k) < , Matrix block diagram of the general linear discrete-time dynamic system.
XJ be vector over some domain X = (Xl x . X,) c 1w’, called the regression the regressor space. The aim then is to identify the static dependence of a on the dependent or response variable, y E Y c R , called the regressand, The concept of system modelling independent variables x, called regressors. 2.
Data engineering : fuzzy mathematics in systems theory and data analysis by Olaf Wolkenhauer