On the design of an ellipsoid ARTMAP classifier within the fuzzy adaptive system ART framework

Peralta, Rolando Grave de; Anagnostopoulos, Georgios C.; Gómez‐Sánchez, Eduardo; Richie, S.M.

doi:10.1109/ijcnn.2005.1555876

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…Therefore, in this study, Ellipsoid ARTMAP (EAM) network in which the node is represented by hyper ellipsoid is presented. In comparison with FAM, hyperellipsoid shape retains virtually all of the excellent characteristics of hyper rectangle, such as batch and incremental learning with fast learning ability, and overcome some of its drawbacks at the same time [30]. The major characteristic of EAM can be summarized as follows.…”

Section: Introductionmentioning

confidence: 99%

Incremental learning for online tool condition monitoring using Ellipsoid ARTMAP network model

Liu

Wang

2015

Applied Soft Computing

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Section: Introductionmentioning

confidence: 99%

Incremental learning for online tool condition monitoring using Ellipsoid ARTMAP network model

Liu

Wang

2015

Applied Soft Computing

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“…In this paper, Ellipsoid-ARTMAP (EAM), which is based on adaptive resonance theory (ART) [16], is presented to realize the task of classifying complex samples due to its strong local and distributed representation ability [17–19]. Unlike Radius Basis Function (RBF) neural network, which is utilized to estimate the probability density of the data streams and judge the occurrence of the fault by comparing the probability density of the current data with the previous one [20], EAM is mainly proposed to depict the spatial distribution of the sample data for each category.…”

Section: Introductionmentioning

confidence: 99%

Vibration Sensor-Based Bearing Fault Diagnosis Using Ellipsoid-ARTMAP and Differential Evolution Algorithms

Liu

Wang

Xie

et al. 2014

Sensors

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Effective fault classification of rolling element bearings provides an important basis for ensuring safe operation of rotating machinery. In this paper, a novel vibration sensor-based fault diagnosis method using an Ellipsoid-ARTMAP network (EAM) and a differential evolution (DE) algorithm is proposed. The original features are firstly extracted from vibration signals based on wavelet packet decomposition. Then, a minimum-redundancy maximum-relevancy algorithm is introduced to select the most prominent features so as to decrease feature dimensions. Finally, a DE-based EAM (DE-EAM) classifier is constructed to realize the fault diagnosis. The major characteristic of EAM is that the sample distribution of each category is realized by using a hyper-ellipsoid node and smoothing operation algorithm. Therefore, it can depict the decision boundary of disperse samples accurately and effectively avoid over-fitting phenomena. To optimize EAM network parameters, the DE algorithm is presented and two objectives, including both classification accuracy and nodes number, are simultaneously introduced as the fitness functions. Meanwhile, an exponential criterion is proposed to realize final selection of the optimal parameters. To prove the effectiveness of the proposed method, the vibration signals of four types of rolling element bearings under different loads were collected. Moreover, to improve the robustness of the classifier evaluation, a two-fold cross validation scheme is adopted and the order of feature samples is randomly arranged ten times within each fold. The results show that DE-EAM classifier can recognize the fault categories of the rolling element bearings reliably and accurately.

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“…In total, Project EMD-MLR has supported the presentations and publications of 2 journal papers, 5 conference papers and 1 invited talk. The interested reader is referred to the corresponding references [10][11][12][13][14][15][16][17][18][19][20][21][22][23] for more information on these products. Finally, we offer access to the produced material via our project's new web portal 9 .…”

Section: S Exact Test For Comparison Of Classification Performances • Analysis Tool For Speech Signal Processing and Recognitionmentioning

confidence: 99%

Engaging Undergraduate Students In Machine Learning Research: Progress, Experiences And Achievements Of Project Emd Mlr

Anagnostopoulos¹,

Georgiopoulos²,

Ports³

et al.

2006 Annual Conference &Amp; Exposition Proceedings

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His research interests are statistical machine learning, neural networks and data mining. Michael Georgiopoulos, University of Central Florida MICHAEL GEORGIOPOULOS is a Professor of the Department of Electrical and Computer Engineering at the University of Central Florida. His research interests lie in the areas of neural networks and applications of neural networks in pattern recognition, image processing, smart antennas and data-mining. He is an Associate Editor of the IEEE Transactions on Neural Networks since 2001. Ken Ports, Florida Tech KEN PORTS is a Professor of the Department of Electrical and Computer Engineering at Florida Tech. He is also the Engineering Director of Florida TechStart, the university business accelerator. His interests include microelectronics, nanoelectronics and radiation effects, entrepreneurial behavior and culture, and business processes such as product to market, strategic planning and execution, and project management. Dr. Ports has 48 publications and 11 patents.

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On the design of an ellipsoid ARTMAP classifier within the fuzzy adaptive system ART framework

Cited by 4 publications

References 11 publications

Incremental learning for online tool condition monitoring using Ellipsoid ARTMAP network model

Incremental learning for online tool condition monitoring using Ellipsoid ARTMAP network model

Vibration Sensor-Based Bearing Fault Diagnosis Using Ellipsoid-ARTMAP and Differential Evolution Algorithms

Engaging Undergraduate Students In Machine Learning Research: Progress, Experiences And Achievements Of Project Emd Mlr

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