This survey samples from the ever-growing family of adaptive resonance theory (ART)
neural network models used to perform the three primary machine learning modalities …

With rapid increase in wind power penetration into the power grid, wind power forecasting is
becoming increasingly important to power system operators and electricity market …

The relationship between driver injury severity and driver, vehicle, roadway, and
environment characteristics was examined. The use of two well-known neural network …

In deregulated electricity markets, price forecasting is gaining importance between various
market players in the power in order to adjust their bids in the day-ahead electricity markets …

The use of machine learning techniques to automatically analyse data for information is
becoming increasingly widespread. In this paper we primarily examine the use of Genetic …

This paper presents a novel hybrid intelligent algorithm utilizing a data filtering technique
based on wavelet transform (WT), an optimization technique based on firefly (FF) algorithm …

In this work it is shown how fuzzy lattice neurocomputing (FLN) emerges as a connectionist
paradigm in the framework of fuzzy lattices (FL-framework) whose advantages include the …

A two-stage hybrid model for data classification and rule extraction is proposed. The first
stage uses a Fuzzy ARTMAP (FAM) classifier with Q-learning (known as QFAM) for …

In this paper, an Ellipsoid ARTMAP (EAM) network model based on incremental learning
algorithm is proposed to realize online learning and tool condition monitoring. The main …

A significant role of a smart grid is to substantially increase the penetration of
environmentally-friendly renewable energy sources, such as solar photovoltaic (PV) power …