Comparing the online learning capabilities of Gaussian ARTMAP and Fuzzy ARTMAP for building energy management systems

Comparing the online learning capabilities of Gaussian ARTMAP and Fuzzy ARTMAP for building energy management systems

Recently, there has been a growing interest in the application of Fuzzy ARTMAP for use in building energy management systems or EMS. However, a number of papers have indicated that there are important weaknesses to the Fuzzy ARTMAP approach, such as sensitivity to noisy data and category proliferation. Gaussian ARTMAP was developed to help overcome these weaknesses, raising the question of whet...

I. Fuzzy Artmap for Probabll..rry Est1mall0n Ii. Fuzzy Artmap

An incremental, nonparametric probability estimation procedure using the fuzzy ARTMAP (adaptive resonance theory-supervised predictive mapping) neural network is introduced. In the slow-learning mode, fuzzy ARTMAP searches for patterns of data on which to build ever more accurate estimates. In max-nodes mode, the network initially learns a fixed number of categories, and weights are then adjust...

Fuzzy ARTMAP, Slow Learning and Probability Estimation

Generalization Performance Analysis between Fuzzy Artmap and Gaussian Artmap Neural Network

This paper examines the generalization characteristic of Gaussian ARTMAP (GAM) neural network in classification tasks. GAM performance for classification during training and testing is evaluated using the k-folds cross validation technique. A comparison is also done between GAM and Fuzzy ARTMAP (FAM) neural network. It is found that GAM performs better (98-99%) when compared to FAM (79-82%) usi...

Fuzzy ARTMAP network with evolutionary learning

Neural networks, particularly the multilayer perceptron, have been used extensively in automated signal classification systems with classification accuracy as the figure of merit. Three important issues that can enhance the utility of these systems are (i) incremental learning, (ii) confidence or reliability measures and (iii) performance improvement through continual learning. This paper inves...