By Francesco Masulli, Sushmita Mitra, Gabriella Pasi
This quantity constitutes the refereed complaints of the seventh overseas Workshop on Fuzzy good judgment and functions held in Camogli, Genoa, Italy in July 2007.
The eighty four revised complete papers provided including three keynote speeches have been conscientiously reviewed and chosen from 147 submissions. The papers are equipped in topical sections on fuzzy set thought, fuzzy info entry and retrieval, fuzzy computer studying, fuzzy architectures and platforms; and precise periods on intuitionistic fuzzy units and gentle computing in picture processing. WILF 2007 hosts 4 designated periods, particularly the Fourth foreign assembly on Computational Intelligence tools for Bioinformatics and Biostatistics (CIDD 2007), the 3rd overseas Workshop on Cross-Language details Processing (CLIP 2007); Intuitionistic Fuzzy units: fresh Advances (IFS), and delicate Computing in picture Processing (CLIPS). those targeted periods expand and deepen the most issues of WILF.
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During the operational one, admissible steps are systematically applied in a similar way to classical resolution steps in pure LP, thus returning an expression where all atoms have been exploited. This last expression is then interpreted under a given lattice during the so called interpretive phase. In declarative programming, it is usual to estimate the computational eﬀort needed to execute a goal by simply counting the number of steps required to reach their solutions. In this paper, we show that although this method seems to be acceptable during the operational phase, it becomes inappropriate when considering the interpretive one.
It is the co–entropy which strongly changes (compare with (15)): E(F ) = 1 M (F ) N m (ωi ) · log m (ωi ) (17) i=1 In particular E(F ) = E(F )−log (F ), and so with respect to the new quantities we have that (and compare with (16)): H (F ) + E (F ) = log |X| M (F ) = log (F ) (F ) (18) In particular, from [H (F ) + log (F )] + E (F ) = log M (F ), we can introduce a new entropy for fuzzy granulation, H (F ) = H (F ) + log (F ), for which trivially one has the expected “invariance” H (F ) + E (F ) = log M (F ) = log |X| .
Consequently, we have that Ic+ (D1 ) = W(&L ) + W(∨G ) + W(@1 ) + W(&P ) = 3 + 1 + 2 + 1 = 7, and Ic+ (D1∗ ) = W(@∗1 ) + W(&P ) = 6 + 1 = 7. Now, both the operational and interpretive cost of derivations D1 and D1∗ do coincide, which is quite natural and realistic if we have into account that rules R1 and R∗1 have the same semantics and, consequently they must also have the same computational behaviour. The previous example shows us that the way in which aggregators are introduced in the body or in the deﬁnition of other aggregators of a program rule, might only reﬂect syntactic preferences, without negative repercussions on computational costs, as our improved deﬁnition of the interpretive cost reﬂects.
Applications of Fuzzy Sets Theory by Francesco Masulli, Sushmita Mitra, Gabriella Pasi