By Sudip Kumar Sahana, Sujan Kumar Saha
This quantity contains the lawsuits of the overseas convention on Computational Intelligence 2015 (ICCI15). This publication goals to collect paintings from prime academicians, scientists, researchers and learn students from around the globe on all facets of computational intelligence. The paintings consists normally of unique and unpublished result of conceptual, optimistic, empirical, experimental, or theoretical paintings in all parts of computational intelligence. Specifically, the most important themes coated comprise classical computational intelligence types and synthetic intelligence, neural networks and deep studying, evolutionary swarm and particle algorithms, hybrid platforms optimization, constraint programming, human-machine interplay, computational intelligence for the net analytics, robotics, computational neurosciences, neurodynamics, bioinspired and biomorphic algorithms, move disciplinary subject matters and functions. The contents of this quantity may be of use to researchers and execs alike.
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Extra resources for Advances in Computational Intelligence: Proceedings of International Conference on Computational Intelligence 2015
9 1 Range Factor are ﬁve linguistic variables for the output fuzzy set ﬁtness; they are very small, small, medium, large, and very large. Fuzzy inference unit uses the fuzzy “if–then” rules for the calculation of output. There are twenty-seven fuzzy mapping rules deﬁned in Table 1. By applying these fuzzy “if–then” mapping rules on the inputs, the fuzzy output range factor has been generated. The output fuzzy variable has no use unless defuzziﬁed to a single crisp value. In this paper, the centre of gravity (COG) method  has been used for the defuzziﬁcation.
1, the various criterions for evaluations are deﬁned. Thereafter, the concept of TIFNs is introduced in Sect. 2 where the values and ambiguities of the membership degree and the non-membership degree for TIFNs as well as the value index and ambiguity index are deﬁned. Section 3 deals with the value and ambiguity-based ranking method to solve multi-attribute decision-making problems in which the ratings of alternatives on attributes are expressed using TIFNs. Section 4 deals with a numerical example and lastly the conclusion is provided in Sect.
Advances in Computational Intelligence: Proceedings of International Conference on Computational Intelligence 2015 by Sudip Kumar Sahana, Sujan Kumar Saha