Fuzzy control has long been applied to industry with several important theoretical results
and successful results. Originally introduced as model-free control design approach, model …

Approximate reasoning systems facilitate fuzzy inference through activating fuzzy if–then
rules in which attribute values are imprecisely described. Fuzzy rule interpolation (FRI) …

Time Series is an orderly sequence of values of a variable in a particular domain.
Forecasting is a challenging task in the area of Time Series Analysis. Forecasting has a …

Fuzzy rule interpolation (FRI) offers an effective approach for making inference possible in
sparse rule-based systems (and also for reducing the complexity of fuzzy models). However …

Many search strategies have been exploited for the task of feature selection (FS), in an effort
to identify more compact and better quality subsets. Such work typically involves the use of …

Fuzzy interpolation does not only help to reduce the complexity of fuzzy models, but also
makes inference in sparse rule-based systems possible. It has been successfully applied to …

In this paper, we present a new method for multi-variable fuzzy forecasting based on fuzzy
clustering and fuzzy rule interpolation techniques. First, the proposed method constructs …

In this paper, we present a new weighted fuzzy interpolative reasoning method for sparse
fuzzy rule-based systems. The proposed method uses weighted increment transformation …

Project selection is a complex decision making process that is influenced by multiple and
often conflicting objectives. The complexity of the project selection problem is due to the high …

As a substantial extension to fuzzy rule interpolation that works based on two neighboring
rules flanking an observation, adaptive fuzzy rule interpolation is able to restore system …