ART 2-A

A streamlined form of the ART 2 network with a drastically accelerated runtime.


Articles & Tech Transfers


Soft-clustering and improved stability for adaptive resonance theory neural networks
Abstract 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 ...

Application of artificial neural networks for the development of a signal monitoring system
Abstract 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 ...

Comparative analysis of fuzzy ART and ART-2A network clustering performance
Abstract 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 ...

Classification of single particles analyzed by ATOFMS using an artificial neural network, ART-2A
Abstract 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, ...

An efficient neural classification chain of SAR and optical urban images
Abstract 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 ...

A proteomic analysis of maize chloroplast biogenesis
Abstract 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 ...

Modified ART 2A growing network capable of generating a fixed number of nodes
Abstract 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 ...

Predicting bulk ambient aerosol compositions from ATOFMS data with ART-2a and multivariate analysis
Abstract 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 ...

ART artificial neural networks based adaptive phase selector
Abstract 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 ...

Identification of long-range aerosol transport patterns to Toronto via classification of back trajectories by cluster analysis and neural network techniques
Abstract 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). ...

Comparison of two cluster analysis methods using single particle mass spectra
Abstract 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 ...

Comparison of an adaptive resonance theory based neural network (ART-2a) against other classifiers for rapid sorting of post consumer plastics by remote near-infrared spectroscopic sensing using an In
Abstract 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 ...

Adaptive resonance theory based neural networks - the ‘ART’ of real-time pattern recognition in chemical process monitoring?
Abstract 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 ...

Airborne Particle Classification with a Combination of Chemical Composition and Shape Index Utilizing an Adaptive Resonance Artificial Neural Network
Abstract 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 ...

POPART: partial optical implementation of adaptive resonance theory 2
Abstract 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 ...

ART 2-A for optimal test series design in QSAR
Abstract 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 ...

ART 2-A: An adaptive resonance algorithm for rapid category learning and recognition
Abstract 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 ...