By M. Azeem
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Additional resources for Fuzzy Inference System - Theory and Applns. [engineering]
For example the system + which is in Defenseless situation will change to entirely different condition (Endangered), point (*), with infinity small deviation in ability to retaliate. Also because of its geometrical structure, this model suffers from the lack of considering additional parameters for risk analysis. Another method which has gained more attraction in the risk analysis literature is the model presented by Eq. (3), based on the linear combination of ability to retaliate and impact threat.
Fuzzy set for the evaluation of the risk of falls from height The representation of continuous fuzzy sets in the Knowledge Base is done using a parametric method that was discussed in (Nunes, I. , 2007). The fuzzification of opinions can use Linguistic Variables (LV). In this approach, due to considerations regarding the numerical efficiency of the computational process, the LV terms were assumed as discrete fuzzy sets. Consider, as an example, the LV “inadequacy” presented in Figure 5, which can be used to evaluate the inadequacy of the protection provided in a workplace, by the Safety Control measures implemented (Nunes, I.
On the other hand, Zadeh does not think that Fuzzy Logic is so in contrast with classical logic. Zadeh and Bellman writes (1977: 109): “Although fuzzy logic represents a significant departure from the conventional approaches to the formalization of human reasoning, it constitutes – so far at least – an extension rather than a total abandonment of the currently held views on meaning, truth and inference”. Fuzzy logic is just an extension of standard Boolean logic: if we keep the fuzzy values at their extremes of 1 (completely true), and 0 (completely false), the laws of classical logic will be valid.
Fuzzy Inference System - Theory and Applns. [engineering] by M. Azeem