is obvious, and then what always hold in the Mamdani's case is the equivalence (p 2 q ! r) = (p ! r) ^ (q ! r): (14) Consequently, if I is a Mamdani–Larsen's implication (8) never holds, but (14) is always true. IV. CONCLUSION After remembering the four main types of implications used in Fuzzy Logic, the equality [p ^ q ! r] = [(p ! r) _ (q ! r)] (*) is analyzed bot in complemented lattices and...

A jilzzy layered neural network/or classification and rule generation is proposed using logical neurons. II can handle uncertainty and/or impreciseness in the input as well as the output. Logical operators, namely, t­ norm T and t-conorm S involving And and Or neurons, are employed in place of the weighted sum and sigmoid functions. Various/uzzy implication operators are introduced to incorpora...

We introduce a new way to visualize the behavior of aggregation operators based on the analogy with a balance. The use of an analogy permits an intuitive representation of the operator. This metaphor is enough general to include operators of the most common aggregation families. So we obtain a general framework, that allows us to compare the different existing operators. In this paper we will s...

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At present time configurable wireless sensor networks are given special consideration. Wireless sensor networks are now a static elements union. To make such a network a powerful system that is able to replace traditional computers, research is required of the application of different theories and technologies for sensor network development. However, building models of approximate reasoning and...

The main purpose of this paper is to consider the t-best simultaneousapproximation in fuzzy normed spaces. We develop the theory of t-bestsimultaneous approximation in quotient spaces. Then, we discuss the relationshipin t-proximinality and t-Chebyshevity of a given space and its quotientspace.