Adaptive Resonance Theory (ART) neural networks model real-time prediction, search, learning, and recognition. ART networks function both as models of human cognitive information processing [1,2,3] and as neural systems for technology transfer [4]. A neural computation central to both the scientific and the technological analyses is the ART matching rule [5], which models the interaction betwee...

Intrusion Detection Systems (IDS) is a key part of system defense, where it identifies abnormal activities happening in a computer system. In general, the traditional intrusion detection relies on the extensive knowledge of security experts, in particular, on their familiarity with the computer system to be protected. To reduce this dependence, various data-mining, soft-computing and machine le...

The performance of artificial vision as a nondestructive technology has been evaluated in monitoring beef meat quality at a storage temperature of 4°C for more than two weeks. A reference method based on bacteriological measurement is performed in parallel with the artificial vision system to analyse the meat samples. Artificial vision data were collected from color image of meat samples in par...

This paper proposes non-parametric measures for the fuzzy ARTMAP computational neural network to handle spatial uncertainty in remotely sensed imagery classification, i.e., ART Commitment (ART-C) and ART Typicality (ART-T), expressing in the first case the degree of commitment a classifier has for each class for a specific pixel, and in the second case, how typical that pixel’s reflectances are...

Fuzzy ARTMAP with Relevance factor (FAMR) is a Fuzzy ARTMAP (FAM) neural architecture with the following property: Each training pair has a relevance factor assigned to it, proportional to the importance of that pair during the learning phase. Using a relevance factor adds more flexibility to the training phase, allowing ranking of sample pairs according to the confidence we have in the informa...

This paper presents an on-line hand-printed character recognition system, tested on datasets produced by the UNIPEN project, thus ensuring sufficient dataset size, author-independence and a capacity for objective benchmarking. New preprocessing and segmentation methods are proposed in order to derive a sequence of strokes for each character, following Ž suggestions of biological models for hand...

Face recognition has become one of the most active research areas of pattern recognition since the early 1990s. This project thesis proposes a novel face recognition method based on Simplified Fuzzy ARTMAP (SFAM). For extracting features to be used for classification, combination of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) is used. This is for improving the capa...

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...

The fusion of multisensorial data is a common practice to identify a world model when data coming from a single sensor is unreliable. A possible approach to multisensor data fusion consists in identifying common interpretations of data coming from different sensors, and to integrate the information represented as symbols related to the possible interpretations. In this paper, we present an arch...