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Available data in the shared volatile memory is imported and taken into account by this module in order to apply business logic decision mechanisms with the values.
An integrated model that relates a fuzzy logic decision support system with cost estimation for concrete box-girder bridges is presented.
As route stability and energy efficiency are the two major issues of routing in MANET, this paper aims to provide stability and energy efficiency to the existing routing protocol using fuzzy logic decision model.
The method combines the use of the logic decision tree and the empirical relationship between the Tectonic Stress Index, TSI, and a series of K in situ values obtained from an extensive database.
At the base station, all one-bit decisions are fused together according to the logic decision fusion rule [27, 28], and the final decision can be obtained as Y = ∑ i = 1 K D i ≥ k H 1 < k H 0, (5).
For any problems in the data that cannot be solved through logic, decision rules will be established.
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In developing the fuzzy logic decision-making system, we first design the rules.
The design of fuzzy logic decision-making system consists of input membership functions and a set of fuzzy rules.
Thus, the fuzzy logic decision-making system offers an efficient solution for this type of uncertain situation.
This makes the fuzzy logic decision-making system a valuable tool for the bionanorobots to determine the diagnosis of the tumor.
Figure 4 shows the steps involved in the design of fuzzy logic decision-making system such as fuzzification of input & output, fuzzy inference engine, and defuzzification.
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