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Unsupervised adaptive resonance theory neural networks for control chart pattern recognition

D T Pham, A B Chan
2001 Proceedings of the Institution of mechanical engineers. Part B, journal of engineering manufacture  
This paper describes the use of unsupervised adaptive resonance theory ART2 neural networks for recognizing patterns in statistical process control charts.  ...  To improve the classi® cation accuracy, three schemes are proposed. The ® rst scheme involves using information on changes between consecutive points in a pattern.  ...  ACKNOWLEDGEMENT The authors would like to thank the European Regional Development Fund (ERDF) for their support for the work described in this paper.  ... 
doi:10.1243/0954405011515136 fatcat:jskqogifqrawfny44kprkzddku

Classification of Eeg Spectrogram using Adaptive Resonance Theory-2

Bindu.R Bindu.R, Dr S G Hiremath Dr S G Hiremath, Shilpa Biradar
2012 International Journal of Scientific Research  
The extracted features were given as the input to the Adaptive Resonance Theory-2 (ART2) classifier.  ...  The extracted features were given as the input to the Adaptive Resonance Theory-2 (ART2) classifier.  ...  In other words: they are always able to learn new patterns without forgetting the past.  ... 
doi:10.15373/22778179/july2014/53 fatcat:7f7wvt2y7bdb3gfe36orpb2bji

Fuzzy ARTMAP based electronic nose data analysis

Eduard Llobet, Evor L Hines, Julian W Gardner, Philip N Bartlett, Toby T Mottram
1999 Sensors and actuators. B, Chemical  
The MLP being by far the most popular neural network method in both the field of EN instruments and elsewhere.  ...  It is Ž a promising method since Fuzzy ARTMAP is able to carry out on-line learning without forgetting previously learnt patterns stable .  ...  In particular, a human brain is able to learn many new events without necessarily forgetting events that occurred in the past.  ... 
doi:10.1016/s0925-4005(99)00288-9 fatcat:5holijpqhzfwpmswehlwgcmccm

Aircraft Engines Remaining Useful Life Prediction with an Improved Online Sequential Extreme Learning Machine

Tarek Berghout, Leïla-Hayet Mouss, Ouahab Kadri, Lotfi Saïdi, Mohamed Benbouzid
2020 Applied Sciences  
In addition, to attempt into addressing dynamic programming based on environmental feedback, a new dynamic forgetting function based on the temporal difference of recursive learning is introduced to enhance  ...  In this paper, a new data-driven learning scheme based on an online sequential extreme learning machine algorithm is proposed for remaining useful life prediction.  ...  In [8] , a new data-driven approach is introduced by Ben Ali et al. by training a Simplified Fuzzy Adaptive Resonance Theory Map (SFAM) neural network with Weibull Distribution (WD) to avoid time domain  ... 
doi:10.3390/app10031062 fatcat:eftjcaum6fc2dedpo33vzdkxxu

Dynamic topology representing networks

J Si, S Lin, M.-A Vuong
2000 Neural Networks  
In the present paper, we propose a new algorithm, namely the Dynamic Topology Representing Networks (DTRN) for learning both topology and clustering information from input data.  ...  The clustering procedure is based on a winner-take-quota learning strategy in conjunction with an annealing process in order to minimize the associated mean square error.  ...  Acknowledgements Research supported in part by NSF under grant  ... 
doi:10.1016/s0893-6080(00)00039-3 pmid:10987515 fatcat:j3aea2s4f5bbhcggip7iakun6a

Fuzzy Neural Network Classification of Global Land Cover from a 1° AVHRR Data Set

Sucharita Gopal, Curtis E. Woodcock, Alan H. Strahler
1999 Remote Sensing of Environment  
The second section discusses prior work using neural network classifiers in  ...  The purpose of this article is to test study shows that artificial neural networks are a viable the utility of a neural network architecture called fuzzy alternative for global scale landcover classification  ...  The orienting subsystem interacts with the attentional subsystem and enables the network to learn The input A is presented to an ART network, and results about novel inputs without forgetting its previous  ... 
doi:10.1016/s0034-4257(98)00088-1 fatcat:mx7itfgngvgulauef7wpx6blja

An ART-based modular architecture for learning hierarchical clusterings

G. Bartfai
1996 Neurocomputing  
In specific, ART networks have the ability to create new output nodes (i.e. categories) dynamically, and do not suffer from the problem of forgetting previously learned categories if the environment changes  ...  In a HART network, each layerwhich is essentially an ART network -learns to cluster the curegory prototypes developed at the layer directly below it.  ...  Acknowledgements I would like to acknowledge the work of both reviewers, and thank the very constructive comments of one of them in particular.  ... 
doi:10.1016/0925-2312(95)00077-1 fatcat:zj7mvyrhjrf7xaf5xx2grkzdfm

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

Gail A. Carpenter, Stephen Grossberg, David B. Rosen
1991 Neural Networks  
This artic,le introduces Adaptive Resonance Theor) 2-A (ART 2-A), an efjCicient algorithm that emulates the self-organizing pattern recognition and hypothesis testing properties of the ART 2 neural network  ...  The speed of ART 2-A makes pructical the use of ART 2 modules in large scale neural computation.  ...  This article introduces ART 2-A. a simple computational system that models the essential dynamics of the ART 2 analog pattern recognition neural network.  ... 
doi:10.1016/0893-6080(91)90045-7 fatcat:nq6qj2gdnjhk5i3ehalliblohq

A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools

Chih-Wen Chang, Hau-Wei Lee, Chein-Hung Liu
2018 Inventions  
Inventions 2018, 3, 41 2 of 28 state automata [4], the continuous time RNN approach to dynamical systems [5], the RNN scheme for long short-term memory (LSTM) [6,7], the echo state network (ESN) approach  ...  , an ML approach to the Bayesian optimization of hyperparameters [60], a clustering algorithm for new distance-based problems [61], Taxonomy and empirical analysis in a supervised learning (SL) scheme  ...  in a neural network.  ... 
doi:10.3390/inventions3030041 fatcat:6qrwhmrl2bfwrgmovqvsyx5p3y

Art networks with geometrical distances

Issam Dagher
2006 Journal of Discrete Algorithms  
In this paper, ART networks (Fuzzy ART and Fuzzy ARTMAP) with geometrical norms are presented. The category choice of these networks is based on the L p norm.  ...  Simulation results on different databases show the good generalization performance of the Fuzzy ARTMAP with L p norm compared to the performance of a typical Fuzzy ARTMAP.  ...  The Fuzzy ART is a neural network architecture that learns in an unsupervised manner [1] to categorize analog input patterns. Two preprocessing stages are applied to the input patterns.  ... 
doi:10.1016/j.jda.2005.06.007 fatcat:j2xznqimlbbwfdmrkv6ynr4goi

Accuracy and Diversity in Ensemble Systems Composed of ARTMAP-Based Models

Araken M. Santos, Anne M.P. Canuto, João C. Xavier Júnior
2008 2008 Eighth International Conference on Hybrid Intelligent Systems  
ARTMAP-based models are neural networks which uses a match-based learning procedure.  ...  Aiming to add an extra contribution to ARTMAP-based ensembles, this paper presents an analysis of accuracy and diversity in these systems.  ...  Match-based learning is distinct from an error-based approach as employed in neural networks such as the standard Multi-Layer Perceptron [6] in which the learning process is based on the error between  ... 
doi:10.1109/his.2008.110 dblp:conf/his/SantosCJ08 fatcat:miacdd3zfbdyxnebn2ofmcsx4i

Self-organizing neural networks for universal learning and multimodal memory encoding

Ah-Hwee Tan, Budhitama Subagdja, Di Wang, Lei Meng
2019 Neural Networks  
This paper shows how a family of biologically-inspired self-organizing neural networks, known as fusion Adaptive Resonance Theory (fusion ART), may provide a viable approach to realizing the learning and  ...  In accordance with the notion of embodied intelligence, such neural models thus provide a computational account of how an autonomous agent may learn and adapt in a real-world environment.  ...  top-down activation (readout operation) 41 achieves the recall task.42 Episodic Encoding: Our approach to encoding a sequence of 43 events in the neural network follows the gradient encoding 44 method  ... 
doi:10.1016/j.neunet.2019.08.020 pmid:31537437 fatcat:35nubypfd5dajijoaalss2z33a

Continuous reinforcement learning with incremental Gaussian mixture models

Rafael Coimbra Pinto
2019 Figshare  
in relation to conventional neural networks.  ...  A single episode is enough to learn the investigated tasks in most trials.  ...  The ART2 Growing Neural Gas (GNG) An improvement over the Neural Gas algorithm [Martinetz and Schulten 1991] , the Growing Neural Gas (GNG) is an unsupervised neural network similar to the SOM,  ... 
doi:10.6084/m9.figshare.9942521 fatcat:jly4alfvtjgy7ktm7pheayvwni

Coordinated machine learning and decision support for situation awareness

N.G. Brannon, J.E. Seiffertt, T.J. Draelos, D.C. Wunsch
2009 Neural Networks  
The current research employs neural networks and Markov chains to process information from sources including sensors, weather data, and law enforcement.  ...  More detailed features of the approach are provided, along with an example force protection scenario.  ...  The vigilance may also require adjustment based on the type and ordering of the learning modes. The core of our machine learning approach is an ART neural network.  ... 
doi:10.1016/j.neunet.2009.03.013 pmid:19395234 fatcat:oeuxs5uro5g4vp4hv5kngctmhi

Survey on Incremental Approaches for Network Anomaly Detection [article]

Monowar H. Bhuyan and D. K. Bhattacharyya and J. K. Kalita
2012 arXiv   pre-print
This paper presents a selective survey of incremental approaches for detecting anomaly in normal system or network traffic.  ...  Anomaly detection systems face many problems including high rate of false alarm, ability to work in online mode, and scalability.  ...  The authors are thankful to the funding agencies.  ... 
arXiv:1211.4493v2 fatcat:vqmysyr2fnfy3bjismgtt4pjku
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