This approach facilitates the representation of uncertain fuzzy set membership functions with rough-fuzzy approximations, thereby improving the flexibility of rule interpolation

This approach facilitates the representation of uncertain fuzzy set membership functions with rough-fuzzy approximations, thereby improving the flexibility of rule interpolation

Pan, “Fuzzy rules interpolation for sparse fuzzy rule-based systems based on interval type-2 Gaussian fuzzy sets and genetic algorithms,” IEEE Trans. Fuzzy

analysis is done by taking connectivity index (order 0, standard) as first descriptor, valence connectivity index (order 0, standard) as second descriptor, dipole

Therefore, observations, interpolated results, contradictions, FICs, and rules are all represented as ATMS nodes in the present work, which are originally assumed to be true and

This paper is the first to demonstrate and simulated a simple and systematic transfer matrix (T-matrix) method for analyzing the accumulated effects of Rayleigh Backscattering

With the introduction of fuzzy models, the discrete Q- learning can be extended to continuous state and action space, which in case of suitably chosen states can lead to

More recently, a fuzzy-rough approach to fuzzy rule induction was presented in [27], where fuzzy reducts are employed to generate rules from data.. This method also employs

The proposed data-driven rule base generation approach for FRI is presented in this section, which is developed using the profile curvature values of different parts of the data

The proposed data-driven rule base generation approach for FRI is presented in this section, which is developed using the profile curvature values of different parts of the data

Reconciliation is more likely to progress when Aboriginal and Torres Strait Islander peoples and non-Indigenous Australians participate equally and equitably in all areas of

The proposed method was evaluated using a syn- thetic dataset and a real-world application, i.e., an in- door environment localisation problem. The first ex- periment demonstrates

There are basically two groups of fuzzy interpolation approaches using the two most common types of fuzzy rule bases (i.e. Mamdani-style rule bases and TSK-style rule bases) to

The thesis has proposed a novel curvature-based sparse rule base generation method to support fuzzy rule interpolation, in an effort to make fuzzy reasoning systems more efficient

The application example clearly shows the benefit of using such a strategy, instead of the 2268 rules in the original FRIQ- learning example application and the 182

Following the introduction of the CPE scheme, the percentage of vehicles violating parking restrictions on weekdays decreased in the commercial areas of all of the towns

Since both approaches involve the analysis of equivalence classes generated from the partitioning of the universe of discourse by sets of features, it is natural, to integrate the

In these case studies, we have shown that IMUL can be used to perform fuzzy rule interpolation for multidimensional input spaces, with advantages over KH and MACI fuzzy