By Dušan Teodorović Ph.D., Katarina Vukadinović (auth.)
ISBN-10: 940105892X
ISBN-13: 9789401058926
ISBN-10: 9401144036
ISBN-13: 9789401144032
When fixing real-life engineering difficulties, linguistic info is frequently encountered that's often tough to quantify utilizing "classical" mathematical innovations. This linguistic info represents subjective wisdom. during the assumptions made by way of the analyst whilst forming the mathematical version, the linguistic details is usually overlooked. however, a variety of site visitors and transportation engineering parameters are characterised via uncertainty, subjectivity, imprecision, and ambiguity. Human operators, dispatchers, drivers, and passengers use this subjective wisdom or linguistic details each day whilst making judgements. judgements approximately direction selection, mode of transportation, best suited departure time, or dispatching vehicles are made via drivers, passengers, or dispatchers. In each one case the choice maker is a human. the surroundings during which a human professional (human controller) makes judgements is in general advanced, making it tough to formulate an appropriate mathematical version. therefore, the advance of fuzzy good judgment structures turns out justified in such events. In convinced events we settle for linguistic info even more simply than numerical info. within the comparable vein, we're completely able to accepting approximate numerical values and making judgements in accordance with them. In a large number of situations we use approximate numerical values completely. it may be emphasised that the subjective estimates of alternative site visitors parameters differs from dispatcher to dispatcher, driving force to driving force, and passenger to passenger.
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Additional resources for Traffic Control and Transport Planning:: A Fuzzy Sets and Neural Networks Approach
Sample text
28. Flight from city A to city C with stopover in city B Let us assume that we have subjectively estimated the daily number of passengers between the cities A and B as follows: Ql is approximately 200; Q2 is approximately 400; Q3 is approximately 300. 29. 29, the level of presumption ex = 0 corresponds to the following confidence intervals of fuzzy numbers: QlO = [150, 250]; Q20 = [350, 450]; Q30 = [250, 350]. Fuzzy numbers Q}, Q2, and Q3 represent triangular fuzzy numbers. In the general case, fuzzy numbers 37 Basic Definitions of the Fuzzy Sets Theory do not have a triangular shape, although triangular fuzzy numbers are most often encountered in examples.
As we have seen, the estimated number of aircraft in parking positions can be expressed by confidence intervals, with each interval characterized by a level of presumption. Let us denote this level of presumption by a. 6; [10, 10], a = 1. 27 shows fuzzy number A. The confidence interval corresponding to level of presumption a is denoted as [aIU, a2U]. 27. lS. ADDING FUZZY NUMBERS Before going into the addition of fuzzy numbers, we will briefly discuss how to add confidence intervals. Let us note the following two confidence intervals: X = [Xl, X2] and Y = [Yl,YZ].
This situation is assigned a level of presumption of o. 6. As we have seen, the estimated number of aircraft in parking positions can be expressed by confidence intervals, with each interval characterized by a level of presumption. Let us denote this level of presumption by a. 6; [10, 10], a = 1. 27 shows fuzzy number A. The confidence interval corresponding to level of presumption a is denoted as [aIU, a2U]. 27. lS. ADDING FUZZY NUMBERS Before going into the addition of fuzzy numbers, we will briefly discuss how to add confidence intervals.
Traffic Control and Transport Planning:: A Fuzzy Sets and Neural Networks Approach by Dušan Teodorović Ph.D., Katarina Vukadinović (auth.)
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