In this paper we describ� Category ART, a variation on the adaptive resonance theory (ART) neural network models. Category ART is a predictive ART architecture because it incorporates an ART module to be able to learn to predict a prescribed category given a prescribed n-dimensional input vector a. In contrast to ARTMAP, Category ART contains only one ART module and the map field algorithm has ...

In this paper we develop techniques that are suitable for the parallel implementation of Fuzzy ARTMAP networks. Speedup and learning performance results are provided for execution on a DECmpp/Sx-1208 parallel processor consisting of a DEC RISC Workstation Front-End (FE) and MasPar MP-1 Back-End (BE) with 8,192 processors. Experiments of the parallel implementation were conducted on the Letters ...

Fuzzy ARTMAP (FAM) is a neural network architecture that can establish the correct mapping between real valued input patterns and their correct labels. FAM can learn quickly compared to other neural network paradigms and has the advantage of incremental/online learning capabilities. Nevertheless FAM tends to slow down as the size of the data set grows. This problem is analyzed and a solution is...

In recent years, electrical systems have evolved, creating uncertainties in short-term economic dispatch programming due to demand fluctuations from self-generating companies. This paper proposes a flexible Machine Learning (ML) approach address load forecasting at various levels of disaggregation the Peruvian Interconnected Electrical System (SEIN). The novelty this includes utilizing meteorol...

We have recently introduced a novel approach to sonarbased object recognition for robotics. The sonar recognition system—consisting of a Polaroid sonar coupled with an A/D data acquisition board in a LINUX-based PC— uses a fuzzy ARTMAP neural network classification system to recognize objects at varying distances. In this article we report additional results in which we test systematically reco...

Neuro-fuzzy systems have been in the focus of recent research as a solution to jointly exploit the main features of fuzzy logic systems and neural networks. Within the application literature, neuro-fuzzy systems can be found as methods for function identification. This approach is supported by theorems that guarantee the possibility of representing arbitrary functions by fuzzy systems. However,...

Although ARTMAP and ART-based models were introduced in early 70’s they were not used in characterizing and classifying ecological observations. ART-based models have been extensively used for classification models based on satellite imagery. This report, to our knowledge, is the first application of ART-based methods and specifically ARTMAP for predicting habitat selection and spatial distribu...

One parametric classify (Maximum likelihood classify, MLC for short) and two non-parametric classifiers (Adaptive resonance theory mappings and this study. Base on the confusion matrix and the pixels fuzzy analysis, the nonparametric classifier may be a more preferable approach than the parametric classifier for some remote sensing applications and deserves further investigation. The ARTMAP cla...