The learning algorithms in many of conventional Neuro-Fuzzy Systems (NFS) are based on
batch or global learning where all parameters of the fuzzy system are optimized off-line …

We recently employed concepts of mathematical morphology to introduce fuzzy
morphological associative memories (FMAMs), a broad class of fuzzy associative memories …

This paper introduces an adaptive fuzzy rule-based system applied as a financial time series
model for volatility forecasting. The model is based on Takagi–Sugeno fuzzy systems and is …

By recent advancements in computer science, using artificial intelligence (AI) has become
popular and attracted many researchers from different fields, including hydrology …

An important application of financial institutions is quantifying the risk involved in investing in
an asset. These are various measures of risk like volatility or value-at-risk. To estimate them …

Nowadays, in Brazil there is a large energy potential that comes from hydro mineral sources,
which most part of the electricity consumed comes from this source. According to this, it is …

The mathematical foundations of fuzzy morphological associative memories (FMAMs) can
be found in mathematical morphology on complete lattices that include the class of fuzzy …

This study presents a prediction system based on evolving fuzzy models and a bottom-up
approach for daily streamflow forecasting. Prediction models are based on adaptive Takagi …

In Brazil, power generation stems mostly from hydroelectric power plants and this is due to
the available geographical conditions. For optimization purposes and economy of these …

Conventional neuro-fuzzy systems used for rainfall-runoff (RR) modelling generally employ
offline learning in which the number of rules and rule parameters need to be set by the user …