This study presents an analysis of two modified fuzzy ARTMAP neural networks. The modifications are first introduced mathematically and their performance is studied on benchmark examples. Then, the performance of the systems is examined on data from the domain of remote sensing. It is shown that each modified ARTMAP system achieves classification accuracy superior to that of standard fuzzy ARTM...

The objective of this paper is to present the strength of fuzzy artmap which is kind of neural networks in the medical field by improving its performance by genetic algorithm. Fuzzy ARTMAP is both much faster and incrementally stable than the other ordinary neural networks models like Multilayer Perceptron. Fuzzy artmap’s parameters have legal range of values that should be determined in the si...

A neural network architecture is introduced for incremental supervised learning of recognition categories and multidimensional maps in response to arbitrary sequences of analog or binary input vectors, which may represent fuzzy or crisp sets of features. The architecture, called fuzzy ARTMAP, achieves a synthesis of fuzzy logic and adaptive resonance theory (ART) neural networks by exploiting a...

This paper shows how knowledge, in the form of fuzzy rules, can be derizted from a superuised learning neural network called fuzzy ARTMAP. Rule extraction proceeds in two stages: pruning, which simplifies the network structure by remooing excessiae recognition categories and weights; and quantization of continuous learned weights, which allows the final system state to be translated into a usab...

A novel adaptive resonance theory (ART) neural network architecture is being proposed. The new model, called Hypersphere ART (H-ART) is based on the same principals like Fuzzy-ART does and, thus, inherits most of its qualities for unsupervised learning. Among these properties is fast, stable, incremental learning on the training set and good generalization on the testing set. While H-ART is int...

Fuzzy ARTMAP is one of the families of the neural network architectures bused on ART(Adaptive Resonance Theory) in which supervised learning can be curried out. However, it usually tends to create more categories than are actually needed. This often causes the so culled overfitting problem, namely the performunce of the networks in test set is not monotonically increasing with the additional tr...

In this paper we introduce new useful, geometric concepts regarding categories in Fuzzy ART and Fuzzy ARTMAP, which shed more light into the process of category competition eligibility upon the presentation of input patterns. First, we reformulate the competition of committed nodes with uncommitted nodes in an F2 layer as a commitment test very similar to the vigilance test. Next, we introduce ...

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 (FAM) is currently considered to be one of the premier neural network architectures in solving classification problems. One of the limitations of Fuzzy ARTMAP that has been extensively reported in the literature is the category proliferation problem. That is, Fuzzy ARTMAP has the tendency of increasing its network size, as it is confronted with more and more data, especially if the...

This paper discusses the application of the newly developed Mahalanobis distance-based ARTMAP (MART) network to the predication of sickle cell anemia patients’ response to Hydroxyurea treatment. Mahalanobis distance-based ARTMAP is a modified version of the Fuzzy ARTMAP networks where the activation and choice function are the same and equal to the Mahalanobis distance between the pattern and t...