A streamlined form of the ART 2 network with a drastically accelerated runtime.
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Stability and plasticity in learning systems are both equally essential, but achieving stability and plasticity simultaneously is difficult. Adaptive resonance theory (ART) neural networks are known for their plastic and ... |
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A prototype of a Signal Monitoring System (SMS) utilizing artificial neural networks is developed in this work. The prototype system is unique in: 1) its utilization of state-of-the-art technology in pattern recognition such ... |
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Adaptive resonance theory (ART) describes a family of self-organizing neural networks, capable of clustering arbitrary sequences of input patterns into stable recognition codes. Many different types of ART-networks have been ... |
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Aerosol particles have received significant public and scientific attention in recent years due to studies linking them to global climatic changes and human health effects. In 1994, Prather et al, (Prather, K. A.; Nordmeyer, ... |
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In this paper a suitable neural classification algorithm, based on the use of Adaptive Resonance Theory (ART) networks, is applied to the fusion and classification of optical and SAR urban images. ART networks provide a ... |
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Proteomics studies to explore global patterns of protein expression in plant and green algal systems have proliferated within the past few years. Although most of these studies have involved mapping of the proteomes of ... |
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This paper introduces the Adaptive Resonance Theory under Constraint (ART-C 2A) learning paradigm based on ART 2A, which is capable of generating a user-defined number of recognition nodes through online estimation of an ... |
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The aerosol time-of-flight mass spectrometry (ATOFMS) has not generally been used to provide a quantitative estimation of chemical compositions of ambient aerosols. In an initial study, the possibility of developing a ... |
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This paper introduces a new phase selector based on adaptive resonance theory (ART). Because conventional phase selector cannot adapt dynamically to the power system operating conditions, it presents different characters ... |
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In this work, back trajectories of air masses arriving in Toronto were classified into distinct transport patterns by cluster analysis and, for the first time, by a neural network (Adaptive Resonance Theory-ART-2a). ... |
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Cluster analysis of aerosol time-of-flight mass spectrometry (ATOFMS) data has been an effective tool for the identification of possible sources of ambient aerosols. In this study, the clustering results of two typical ... |
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An Adaptive Resonance Theory Based Artificial Neural Network (ART-2a) has been compared with Multilayer Feedforward Backpropagation of Error Neural Networks (MLF-BP) and with the SIMCA classifier. All three classifiers were ... |
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The family of artificial neural networks based on Adaptive Resonance Theory (ART) forms a collection of distinct mathematical pattern recognition methods. The classification of sensor signals, process data analysis, spectral ... |
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Airborne particle classification that leads to particle source identification is important to both the improvement of the environment and the protection of public health. In this study, individual airborne particles were ... |
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Adaptive resonance architectures are neural nets that are capable of classifying arbitrary input patterns into stable category representations. A hybrid optoelectronic implementation utilizing an optical joint transform ... |
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The family of adaptive resonance theory (ART) based systems concerns distinct artificial neural networks for unsupervised and supervised clustering analysis. Among them, the ART 2-A paradigm presents numerous strengths for ... |
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This article introduces Adaptive Resonance Theory 2-A (ART 2-A), an efficient algorithm that emulates the self-organizing pattern recognition and hypothesis testing properties of the ART 2 neural network architecture, but at ... |
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