This paper focuses on classification problems, and in particular on the evolution of ARTMAP architectures using genetic algorithms, with the objective of improving generalization performance and alleviating the adaptive resonance theory (ART) category proliferation problem. In a previous effort, we introduced evolutionary fuzzy ARTMAP (FAM), referred to as genetic Fuzzy ARTMAP (GFAM). In this p...

In this paper we introduce novel geometric concepts, namely category regions, in the original framework of Fuzzy-ART (FA) and Fuzzy-ARTMAP (FAM). The definitions of these regions are based on geometric interpretations of the vigilance test and the F2 layer competition of committed nodes with uncommitted ones, that we call commitment test. It turns out that not only these regions have the same g...

Data mining techniques have been used by researchers for analyzing protein sequences. In protein analysis, especially in protein sequence classification, selection of feature is most important. Popular protein sequence classification techniques involve extraction of specific features from the sequences. Researchers apply some well-known classification techniques like neural networks, Genetic al...

In this paper, combination between a Fuzzy ARTMAP-based artificial neural network (ANN) model and the genetic algorithm (GA) for performing cooperative learning is described. In our previous work, we have proposed a hybrid network integrating the Fuzzy ARTMAP (FAM) network with the Dynamic Decay Adjustment (DDA) algorithm (known as FAMDDA) for tackling pattern classification tasks. In this work...

In this paper, the gain in LD-CELP speech coding algorithm is predicted using three neural models, that are equipped by genetic and particle swarm optimization (PSO) algorithms to optimize the structure and parameters of neural networks. Elman, multi-layer perceptron (MLP) and fuzzy ARTMAP are the candidate neural models. The optimized number of nodes in the first and second hidden layers of El...

Location awareness is the key capability of mobile computing applications. Despite high demand, indoor location technologies have not become truly ubiquitous mainly due to their requirements of costly infrastructure and dedicated hardware components. Received signal strength (RSS) based location systems are poised to realize economical ubiquity as well as sufficient accuracy for variety of appl...

Distributed coding at tbe hidden layer of a multi-layer perceptron (MLP) endows the network with memory compression and noise tolerance capabilities. However, an MLP typically requires slow off-line learning to avoid catastrophic forgetting in an open input environment. An adaptive resonance theoty (ART) model is designed to guarantee stable memories even with fast online learning, However, ART...

Adaptive Resonance Theory (ART) neural network architectures, such as Fuzzy ARTMAP (FAM), have solved successfully a variety of classification problems. However, FAM suffers from an inherent problem that of creating larger architectures than it is necessary to solve the problem at hand (referred to as the ART category proliferation problem). This problem is especially amplified for classificati...

This paper is focused on the data clustering using the ART-like neural network. Firstly is concerned with the problems from the theoretical point of view by explaining what the clustering is, giving an idea of accumulation methods and mentioning the place of the neural networks in the data clustering. From among the neural network it is focused on the MF ARTMAP. Firstly it explains the principl...