By Bilal M. Ayyub
Uncertainty has been a priority to engineers, managers, and scientists for a few years in engineering and sciences. Uncertainty has for a very long time been thought of synonymous with random, stochastic, statistic, or probabilistic. because the early sixties perspectives on uncertainty became extra heterogeneous and extra instruments that version uncertainty than facts were proposed by means of a number of engineers and scientists. The device/ option to version uncertainty in a selected context should still relatively be offerings by means of contemplating the gains of the phenomenon into account no longer independently of what's identified in regards to the approach and what motives uncertainty. utilized examine in Uncertainty Modeling research concentrates on common features of uncertainty, modeling, and techniques, and comprises huge numbers of examples on engineering and sciences.
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Additional resources for Applied Research in Uncertainty Modeling and Analysis (International Series in Intelligent Technologies)
1977. “Fuzzy Measures and Fuzzy Integrals: A Survey,” In Gupta, M. , Saridis, G. , and Gaines, B. R. ), Fuzzy Automata and Decision Processes, NorthHolland, Amsterdam and New York, 89-102. , and Klir, G. , 1992. Fuzzy Measure Theory, Plenum Press, New York. , and Weinstein, M. , 1978. ” In R. A. ) Research in the Sociology of Knowledge, Science and Art, Volume 1, JAI Press, NY. Zadeh, L. , 1965, “Fuzzy Sets,” Information and Control, 8, 338-353. Zadeh, L. , 1987, “Fuzzy Sets as a Basis for a Theory of Possibility,” Fuzzy Sets and Systems, 1, 3-28.
Let and Then and We now substitute for to get the of fuzzy number Add the point estimate, when for the 0% confidence interval. 01 (99% confidence interval) to one (0% confidence interval) we get the fuzzy number for We drop the graph straight down at the ends to obtain a complete fuzzy number. Example 1 Suppose and we obtained We evaluated equations (8) through (10) using Maple  and then the graph of is shown in Figure 2, without dropping the graph straight down to the at the end points. However, in the rest of the paper we will use a triangular fuzzy number for Simulation of Fuzzy Systems I Figure 2: Fuzzy Arrival Rate 35 in Example 1.
Self-organizing the weights according to the following condition by the method described 28 Noriyasu Homma, et al Case 1: If the candidate output awareness weight so that rule given in Eq. (23). Case 2: If is equal to 1, try to create a new by the extended Hebbian create new neuron m ( k ) +1 with the same weight vector and Then c = m(k) and learn the weight to delete unnecessary connections by using Eq. (21). if there is a neuron without any connections, Case 3: In the case of then select this neuron for a candidate.
Applied Research in Uncertainty Modeling and Analysis (International Series in Intelligent Technologies) by Bilal M. Ayyub