Granular structure plays a very important role in the model construction, theoretical analysis and algorithm design of a granular computing method. The granular structures of classical rough sets and fuzzy rough sets have been proven to be clear. In classical rough set theory, equivalence classes are basic granules, and the lower and upper approximations of a set can be computed by those basic ...

Fuzzy Logic Systems are widely recognized to be successful at modelling uncertainty in a large variety of applications. While recently interval type-2 fuzzy logic has been credited for the ability to better deal with large amounts of uncertainty, general type2 fuzzy logic has been a steadily growing research area. All fuzzy logic systems require the accurate specification of the membership func...

A generalization of an (∈, ∈ ∨ q)-fuzzy bi-filter (resp. (∈, ∈ ∨ q)fuzzy bi-filter) of an ordered semigroup is discussed. Characterizations of an (∈, ∈ ∨ qk)-fuzzy bi-filter and an (∈, ∈ ∨ qk)-fuzzy bi-filter are provided. Relations between fuzzy bi-filters and (∈, ∈ ∨ qk)-fuzzy bifilters (resp. (∈, ∈ ∨ qk)-fuzzy bi-filters) are described. Conditions for an (∈, ∈∨ qk)-fuzzy bi-filter (resp. (∈,...

By using the concepts of fuzzy number fuzzy measures and fuzzy valued functions a theory of fuzzy integrals is investigated. In this paper we have established the fuzzy version of Generalised monotone Convergence theorem and generalised Fatous lemma. 1. Introduction In the preceding paper [2], it is introduced that a concept of fuzzy number fuzzy measures, defined the fuzzy integral of a functi...

Hori (né Uemura) et al. formulated the fuzzy-Bayes decision rule extension of the Wald decision function to fuzzy ORand AND-connectives with multiple subjective distributions. This decision rule maps and transforms a state of nature to fuzzy events. In this context, the map of subjective distributions is called the subjective possibility distribution and the map of utility functions is called t...

In this paper a new backpropagation learning method enhanced with type-2 fuzzy logic is presented. Simulation results and a comparative study among monolithic neural networks, neural network with type-1 fuzzy weights and neural network with type-2 fuzzy weights are presented to illustrate the advantages of the proposed method. In this work, type-2 fuzzy inference systems are used to obtain the ...

We present an application of type-2 neuro-fuzzy modeling to stock price prediction based on a given set of training data. Type-2 fuzzy rules can be generated automatically by a self-constructing clustering method and the obtained type-2 fuzzy rules cab be refined by a hybrid learning algorithm. The given training data set is partitioned into clusters through input-similarity and output-similari...

A method for response integration in modular neural networks with type-2 fuzzy logic for biometric systems p. 5 Evolving type-2 fuzzy logic controllers for autonomous mobile robots p. 16 Adaptive type-2 fuzzy logic for intelligent home environment p. 26 Interval type-1 non-singleton type-2 TSK fuzzy logic systems using the hybrid training method RLS-BP p. 36 An efficient computational method to...