Fuzziness is an inherent property of geographical phenomena and the processes data acquisition, processing, analysis often introduce uncertainty. Existing methods predominantly use fuzzy set (FS) theory to capture fuzziness as spatial objects. However, this approach has a conceptual confusion regarding fuzziness, uncertainty, vagueness, membership degree expressed using accurate values that ign...

The decision-making process on investing in financial markets is a very complex and difficult task, mainly due to the chaotic behavior high uncertainty development of prices investment instruments. For this reason, are increasingly using means artificial intelligence, namely fuzzy logic, which able capture nonlinear behavior.Fuzzy logic provides way draw definitive conclusions from vague, ambig...

A comparison of different T-norms and S-norms for interval type-2 fuzzy number weights is proposed in this work. The interval type-2 fuzzy number weights are used in a neural network with an interval backpropagation learning enhanced method for weight adjustment. Results of experiments and a comparative research between traditional neural networks and the neural network with interval type-2 fuz...

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...