Generalized composition of binary aggregation operators
We study a construction method for binary aggregation operators that generalizes the classical composition. Several examples are given, specially, in the class of aggregation operators with neutral element 1, e.g. semicopulas and copulas.
Recursive and iterative OWA operators
An important issue when using the OWA aggregation operators is the determination of weights. One approach is to link the weights to a desired attitudinal character for the aggregation. The ME-OWA operators provide a pioneering example of this approach. ...
Inner product truth-valued flow inference
Inference problems are one of the main research topics in the artificial intellect field. So far there have been various inference systems, some of them have been applied in fuzzy control according to their feature. In 1989, the concept of truth-valued ...
A note on fixed point theorem for fuzzy mappings
This paper explores Heilpern's notions of fuzzy mapping and the fixed point theorem for fuzzy mappings. The fixed point theorem for fuzzy mappings as introduced by Heilpern has been generalized and some characterizations are done in this context.
On arithmetic operations of interval numbers
In this paper, by using Sengupta and Pal's method of comparison of interval numbers and a new set of arithmetic operations for interval numbers, we propose a theory for the study of arithmetic operations on interval numbers.
A fuzzy data mining algorithm for incremental mining of quantitative sequential patterns
In real world applications, the databases are constantly added with a large number of transactions and hence maintaining latest sequential patterns valid on the updated database is crucial. Existing data mining algorithms can incrementally mine the ...
The knowledge-based fuzzy rules emulated network and its applications on direct adaptive on nonlinear control systems
This paper proposes an adaptive network architecture, which can emulate the human knowledge as the fuzzy logic rule, and its applications as the controller for nonlinear systems. The structure of this proposed network, multi-input Fuzzy Rule Emulated ...
Fuzzy rule extraction from a feed forward neural network by training a representative fuzzy neural network using gradient descent
Neural networks are good at representing functions or data transformations. However just as in the case of the biological brain the mathematical description of the data transformation is hidden. In the case of the human brain the transformation, in ...
