By Bernd Möller
Observations of uncertainty in measured facts with time improves forecasting power in quite a lot of fields in engineering. This e-book offers an creation to uncertainty forecasting according to fuzzy time sequence. It info descriptive, modeling, and forecasting tools for fuzzy time sequence. assurance areas emphasis on forecasting in accordance with fuzzy random approaches in addition to forecasting regarding fuzzy neuronal networks.
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38). ✔ a ☎☎☎ ✖. ˜ ✏ ✕ .. . A❞x 0 ✜ 0 .. ✣ ❞ x x . 38) ☎☎☎ a Furthermore, special fuzzy addition and subtraction operators are required. 10. The symbols ❵ and ❛ represent the lα rα -addition and lα rα subtraction of the fuzzy variables x ˜ and y˜ according to Eq. 39). 39) The fuzzy result variable z˜ must also comply with Eq. 18). The corresponding conditions are given by Eq. 40), whereby the upper operators are applied for the lα rα -addition and the lower for the lα rα -subtraction. The requirement of non-negativity must likewise be fulfilled except at the peak point.
For a concrete sample of fuzzy variables an empirical frequency distribution may also be evaluated. 5 Äxá2l Fig. 17. Empirical fuzzy probability distribution function form II coordinate system of the increments for the exemplary sample ˆ ♣x ˜q in the lr Fs continuous fuzzy random variables. This procedure, which is explained in the following, requires an evaluation of the relative frequencies of the realizations of the fuzzy random variable. , a ˜m of m different fuzzy variables, the following definition holds.
In the context of fuzzy time series, however, only the final fuzzy result variables of a sequence of arithmetic operations must be fuzzy variables in the proper sense. This is a basic condition for the analysis and forecasting of fuzzy time series. The following two examples illustrate the different kinds of subtraction of fuzzy variables. 15. The first example demonstrates the fundamental distinction between the lα rα -subtraction, the HUKUHARA difference, and the subtraction according the extension principle.