Probabilistic simplified fuzzy ARTMAP (PSFAM)

Jervis, B.W.; Garcia, Tanya P.; Giahnakis, Emmanuel

doi:10.1049/ip-smt:19990383

Cited by 18 publications

(9 citation statements)

References 12 publications

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“…While the SFAM performs very well for this application, the more recent development of the Probabilistic SFAM (PSFAM) [3] offers additionally a quantitative prediction of the Bayes Posterior Probability that a test vector (hence a fault) belongs to the predicted class, thereby lending a measure of confidence to the prediction.…”

Section: Discussion and Future Workmentioning

confidence: 99%

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Fault diagnosis for a delta-sigma converter by a neural network

Jervis

Holding

2004

First International Symposium on Control, Communications and Signal Processing, 2004.

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The diagnosis of faults in a first order A-o converter is described. The circuit behaviour of fault-free circuits and circuits containing single faults were simulated and characterized by the output bitstream patterns. The latter were compared with that of the ideal fault-free circuit. A Simplified Fuzzy ARTMAP was trained with metrics derived from the bitstreams and their assigned class. A diagnostic accuracy of 93% was achieved using just two of the metrics. The technique might be useful for the diagnosis of other circuits.

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Section: Discussion and Future Workmentioning

confidence: 99%

“…The Simplified Fuzzy ARTMAP (SFAM) [ 11 was derived from the Fuzzy ARTMAF' [ 2 ] , and is also described in [3]. The SFAM may be rapidly trained with just one pass of the training data, and training may be interspersed between use for testing.…”

Section: Simplified Fuzzy Artmap (Sfam)mentioning

confidence: 99%

Fault diagnosis for a delta-sigma converter by a neural network

Jervis

Holding

2004

First International Symposium on Control, Communications and Signal Processing, 2004.

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“…The PSFAM, used to classify the data, consisted of a Simplified Fuzzy ARTMAP (SFAM) and a Bayes classifier. 8 The latter produced the Bayes posterior probability P ( A | X ) that the test vector X belonged to the class AD or class normal.…”

Section: Theoretical Aspectsmentioning

confidence: 99%

“…1, 7 Thus, only the essentials of that work are repeated here. A selective analysis of those data using an artificial neural network, the Probabilistic Simplified Fuzzy ARTMAP (PSFAM), 8 and a voting strategy is presented.…”

Section: Introductionmentioning

confidence: 99%

New-Onset Alzheimer’s Disease and Normal Subjects 100% Differentiated by P300

Jervis¹,

Bigan

Jervis³

et al. 2019

Am J Alzheimers Dis Other Demen

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Previous work has suggested that evoked potential analysis might allow the detection of subjects with new onset Alzheimer's Disease, which would be useful clinically and personally. Here, it is described how new-onset Alzheimer's disease subjects have been differentiated from healthy, normal subjects to 100% accuracy, based on the back-projected independent components (BICs) of the P300 peak at the EEG electrodes in the response to an oddball, auditory evoked potential paradigm. After artefact removal, clustering, selection, and normalisation processes the BICs were classified using a neural network, a Bayes classifier, and a voting strategy. The technique is general and might be applied for pre-symptomatic detection, and to other conditions and evoked potentials, although further validation with more subjects, preferably in multi-centre studies is recommended.

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“…The classifier used herein is simplified fuzzy ARTMAP (SFAM) (Kasuba, 1993) which was derived from the fuzzy ARTMAP (Carpenter et al, 1992) network. SFAM provides a lower training time and higher recognition accuracy in comparison to other traditional neural networks (Palaniappan & Eswaran, 2009;Jervis et al, 1999); therefore, it has been applied in numerous classification problems (Palaniappan & Eswaran, 2009;Rajasekaran & Pai, 2000a;Rajasekaran & Pai, 2000b;Vuskovic & Du, 2002). (Feldman, 2006;Feldman, 2011).…”

Section: Introductionmentioning

confidence: 99%

An application to transient current signal based induction motor fault diagnosis of Fourier–Bessel expansion and simplified fuzzy ARTMAP

Tran

Althobiani

Ball

et al. 2013

Expert Systems with Applications

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The start-up transient signals have been widely used for fault diagnosis of induction motor because they can reveal early defects in the development process, which are not easily detected with the signals in the steady state operation. However, transient signals are non-linear and contain multi components which need a suitable technique to process and identify the fault pattern. In this paper, the fault diagnosis problem of induction motor is conducted by a data driven framework where the Fourier-Bessel (FB) expansion is used as a tool to decompose transient current signal into series of single components. For each component, the statistical features in the time and the frequency domains are extracted to represent the characteristics of motor condition. The high dimensionality of the feature set is solved by generalized discriminant analysis (GDA) implementation to decrease the computational complexity of classification. In the meantime, with the aid of GDA, the separation of the feature clusters is increased, which enables the more classification accuracy to be achieved. Finally, the reduced dimensional features are used for classifier to perform the fault diagnosis results. The classifier used in this framework is the simplified fuzzy ARTMAP (SFAM) which belongs to a special class of neural networks (NNs) and provides a lower training time in comparison to other traditional NNs. The proposed framework is validated with transient current signals from an induction motor under different conditions including bowed rotor, broken rotor bar, eccentricity, faulty bearing, mass unbalance and phase unbalance. Additionally, this paper provides the comparative performance of (i) SFAM and support vector machine (SVM), (ii) SVM in the framework and SVM combined with wavelet transform in previous studies, (iii) the use of FB decomposition and Hilbert transform decomposition. The results show that the proposed diagnosis framework is capable of significantly improving the classification accuracy.

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Probabilistic simplified fuzzy ARTMAP (PSFAM)

Cited by 18 publications

References 12 publications

Fault diagnosis for a delta-sigma converter by a neural network

Fault diagnosis for a delta-sigma converter by a neural network

New-Onset Alzheimer’s Disease and Normal Subjects 100% Differentiated by P300

An application to transient current signal based induction motor fault diagnosis of Fourier–Bessel expansion and simplified fuzzy ARTMAP

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