Dealing with uncertainty is one of the most critical problems in complicatedpattern recognition subjects. In this paper, we modify the structure of a useful UnsupervisedFuzzy Neural Network (UFNN) of Kwan and Cai, and compose a new FNN with 6 types offuzzy neurons and its associated self organizing supervised learning algorithm. Thisimproved five-layer feed forward Supervised Fuzzy Neural Netwo...

A reliable characterisation of uncertainties can aid uncertainty identification during environmental risk assessments (ERAs). However, typologies can be implemented inconsistently, causing uncertainties to go unidentified. We present an approach based on nine structured elicitations, in which subject-matter experts, for pesticide risks to surface water organisms, validate and assess three dimen...

Reduced-order battery lifetime models, which consist of algebraic expressions for various aging modes, are widely utilized extrapolating degradation trends from accelerated tests to real-world scenarios. Identifying models with high accuracy and low uncertainty is crucial ensuring that model extrapolations believable, however, it difficult compose accurately predict multivariate data trends; a ...

In this paper, a revisited approach for fuzzy regression linear model representation and identification is introduced. By adopting the commonly used principle of D-cuts, the fuzzy regression implementation is reduced to the handling of conventional intervals, for inputs, parameters and outputs. Using the Midpoint-Radius representation of intervals, the uncertainty attached to linear models beco...