In this paper, the combinations of fuzzy system theory with the finite element methods are presen... more In this paper, the combinations of fuzzy system theory with the finite element methods are present and discuss to deal with the uncertainties. The present of uncertainties is needed to avoid for prevent the failure of the material in engineering. There are three types of uncertainties, which are stochastic, epistemic and error uncertainties. In this paper, the epistemic uncertainties have been considered. For the epistemic uncertainty, it exists as a result of incomplete information and lack of knowledge or data. Fuzzy system theory is a non-probabilistic method, and this method is most appropriate to interpret the uncertainty compared to statistical approach when the deal with the lack of data. Fuzzy system theory contains a number of processes started from converting the crisp input to fuzzy input through fuzzification process and followed by the main process known as mapping process. The term mapping here means that the logical relationship between two or more entities. In this study, the fuzzy inputs ...
In this paper, the combinations of fuzzy system theory with the finite element methods are presen... more In this paper, the combinations of fuzzy system theory with the finite element methods are present and discuss to deal with the uncertainties. The present of uncertainties is needed to avoid for prevent the failure of the material in engineering. There are three types of uncertainties, which are stochastic, epistemic and error uncertainties. In this paper, the epistemic uncertainties have been considered. For the epistemic uncertainty, it exists as a result of incomplete information and lack of knowledge or data. Fuzzy system theory is a non-probabilistic method, and this method is most appropriate to interpret the uncertainty compared to statistical approach when the deal with the lack of data. Fuzzy system theory contains a number of processes started from converting the crisp input to fuzzy input through fuzzification process and followed by the main process known as mapping process. The term mapping here means that the logical relationship between two or more entities. In this study, the fuzzy inputs are numerically integrated based on extension principle method. In the final stage, the defuzzification process is implemented. Defuzzification is an important process to allow the conversion of the fuzzy output to crisp outputs. Several illustrative examples are given and from the simulation, the result showed that propose the method produces more conservative results comparing with the conventional finite element method.
In this paper, the combinations of fuzzy system theory with the finite element methods are presen... more In this paper, the combinations of fuzzy system theory with the finite element methods are present and discuss to deal with the uncertainties. The present of uncertainties is needed to avoid for prevent the failure of the material in engineering. There are three types of uncertainties, which are stochastic, epistemic and error uncertainties. In this paper, the epistemic uncertainties have been considered. For the epistemic uncertainty, it exists as a result of incomplete information and lack of knowledge or data. Fuzzy system theory is a non-probabilistic method, and this method is most appropriate to interpret the uncertainty compared to statistical approach when the deal with the lack of data. Fuzzy system theory contains a number of processes started from converting the crisp input to fuzzy input through fuzzification process and followed by the main process known as mapping process. The term mapping here means that the logical relationship between two or more entities. In this study, the fuzzy inputs ...
In this paper, the combinations of fuzzy system theory with the finite element methods are presen... more In this paper, the combinations of fuzzy system theory with the finite element methods are present and discuss to deal with the uncertainties. The present of uncertainties is needed to avoid for prevent the failure of the material in engineering. There are three types of uncertainties, which are stochastic, epistemic and error uncertainties. In this paper, the epistemic uncertainties have been considered. For the epistemic uncertainty, it exists as a result of incomplete information and lack of knowledge or data. Fuzzy system theory is a non-probabilistic method, and this method is most appropriate to interpret the uncertainty compared to statistical approach when the deal with the lack of data. Fuzzy system theory contains a number of processes started from converting the crisp input to fuzzy input through fuzzification process and followed by the main process known as mapping process. The term mapping here means that the logical relationship between two or more entities. In this study, the fuzzy inputs are numerically integrated based on extension principle method. In the final stage, the defuzzification process is implemented. Defuzzification is an important process to allow the conversion of the fuzzy output to crisp outputs. Several illustrative examples are given and from the simulation, the result showed that propose the method produces more conservative results comparing with the conventional finite element method.
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Papers by Eusz Nur
to deal with the uncertainties. The present of uncertainties is needed to avoid for prevent the failure of the material in
engineering. There are three types of uncertainties, which are stochastic, epistemic and error uncertainties. In this paper,
the epistemic uncertainties have been considered. For the epistemic uncertainty, it exists as a result of incomplete
information and lack of knowledge or data. Fuzzy system theory is a non-probabilistic method, and this method is most
appropriate to interpret the uncertainty compared to statistical approach when the deal with the lack of data. Fuzzy system
theory contains a number of processes started from converting the crisp input to fuzzy input through fuzzification process
and followed by the main process known as mapping process. The term mapping here means that the logical relationship
between two or more entities. In this study, the fuzzy inputs are numerically integrated based on extension principle
method. In the final stage, the defuzzification process is implemented. Defuzzification is an important process to allow
the conversion of the fuzzy output to crisp outputs. Several illustrative examples are given and from the simulation, the
result showed that propose the method produces more conservative results comparing with the conventional finite
element method.
to deal with the uncertainties. The present of uncertainties is needed to avoid for prevent the failure of the material in
engineering. There are three types of uncertainties, which are stochastic, epistemic and error uncertainties. In this paper,
the epistemic uncertainties have been considered. For the epistemic uncertainty, it exists as a result of incomplete
information and lack of knowledge or data. Fuzzy system theory is a non-probabilistic method, and this method is most
appropriate to interpret the uncertainty compared to statistical approach when the deal with the lack of data. Fuzzy system
theory contains a number of processes started from converting the crisp input to fuzzy input through fuzzification process
and followed by the main process known as mapping process. The term mapping here means that the logical relationship
between two or more entities. In this study, the fuzzy inputs are numerically integrated based on extension principle
method. In the final stage, the defuzzification process is implemented. Defuzzification is an important process to allow
the conversion of the fuzzy output to crisp outputs. Several illustrative examples are given and from the simulation, the
result showed that propose the method produces more conservative results comparing with the conventional finite
element method.