Terminological difficulties in fuzzy set theory-the case of Intuitionistic Fuzzy Sets Fuzzy Sets and Systems, 156 (2005), pp. 485-491 View PDFView articleView in ScopusGoogle Scholar [13] F. Esteva, L. Godo Monoidal t-norm based logic: towards a logic for left-continuous t-norms Fuzzy Set...
The last direction is the space of measurable functions and the space of (Sugeno)-integrable functions based on the theory of monotonic measures. In this paper, we also present several suggestions on future research. Wu Congxin was an undergraduate student of the Mathematics Department of ...
Crestani, F., Pasi, G.: Soft Information Retrieval: Applications of Fuzzy Set Theory and Neural Networks. In: Kasabov, N., Kozma, R. (eds.) Neuro-Fuzzy Techniques for Intelligent Information Systems, pp. 287–315. Physica Verlag (Springer Verlag), Heidelberg (1999) Hollmen, J.: Self ...
Extension and intensions in the rough set theory Information Sciences, 107 (1998), pp. 149-167 View PDFView articleGoogle Scholar [5] K. Chakrabarty, R. Biawas, S. Nanda Fuzziness in rough sets Fuzzy Sets and Systems, 10 (2000), pp. 247-251 View PDFView articleGoogle Scholar [6] C...
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With this modelization, what is achieved is that the re- lation between different interval-valued mea- sures of information is a copy of the relation that exists between these concepts in fuzzy set theory. However, we consider that in this case we lose the information that would be pro- ...
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N is the number of input features; Ajn denotes the fuzzy set for rule Rj and nth feature, and yj is the consequent function of rule Rj , that is, a linear combination of the elements of x , with coefficients aj and a constant bj. To assess to which degree each of the fuzzy ...
View PDFView articleGoogle Scholar [69] D. Zeng, K. Sycara How can an agent learn to negotiate? J.P. Müller, M.J. Wooldridge, N.R. Jennings (Eds.), Intelligent Agents III: Agent Theories, Architectures, and Languages, Lecture Notes in Artificial Intelligence, 1193, Springer, Berlin (...
In contrast to the fuzzy set theory, in IFS the data information assigns a membership degree, a non- membership degree and a hesitancy degree to each component. As stated in Xu & Liao's research19, triangular fuzzy numbers, trapezoidal fuzzy numbers and interval-valued fuzzy numbers can only...