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

Keyvan, Shahla; Durg, Ajaya; Nagaraj, J.

doi:10.1111/1468-0394.00044

Cited by 10 publications

(6 citation statements)

References 7 publications

(4 reference statements)

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“…Neural networks have been popularly employed as multidimensional classifiers in nonlinear domain for quite some time now (Shi et al, 1996;Keyvan et al, 1997;Zhou and Xu, 1999;Turkoglu et al, 2003;Zhou et al, 2003;Dutta et al, 2009;Karhk et al, 2009;Ubeyli, 2009;Wang et al, 2010;Cortez et al, 2012). The backpropagation algorithm of NN, traditionally the most popular training algorithm for NN, searches for the minimum of an error function in weight space using the method of gradient descent.…”

Section: Neural Network-based Classifiersmentioning

confidence: 99%

“…In recent times, wavelet transform (WT) has been widely used as a popular feature extraction tool (Turkoglu et al, 2003;Huang and Wu, 2008;Ubeyli, 2008;Hussain et al, 2009). Similarly, artificial neural networks (ANN) (Keyvan et al, 1997;Turkoglu et al, 2003;Chatterjee et al, 2009;Dutta et al, 2009;Karhk et al, 2009;Ubeyli, 2009;Gorunescu et al, 2011;Cortez et al, 2012) and fuzzy logic-based systems (Feng and Xu, 1999;Zhou and Xu, 2001;Lee, 2002;Patra et al, 2009;Zhou et al, 2009) have been successfully used for the classification of various types of signals, based on suitably extracted features. WT has the capability of extracting information from a signal in both time and frequency domain simultaneously and has been applied, in the past, for detection and classification of power system transients.…”

Section: Introductionmentioning

confidence: 99%

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A dual‐tree complex wavelet transform‐based approach for recognition of power system transients

Chakraborty¹,

Chatterjee

Goswami

2014

Expert Systems

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The present paper proposes a dual‐tree complex wavelet transform (DTCWT) based approach for recognition of power system transients. Several researchers, all over the world, have so far attempted to solve the problems of recognition of power system transients, hybridizing transform‐based techniques with popular computational intelligence based tools, for example, using wavelet transform and S‐transform for feature extraction, followed by artificial neural networks (ANN) or fuzzy logic‐based classifiers. The proposed method of hybridizing DTCWT‐based feature extraction with ANN‐based classification could efficiently detect several commonly occurring power quality (PQ) disturbance events. The PQ disturbance events considered include four different transient conditions, namely transients due to capacitor switching, transformer inrush currents, transients due to motor switching and transients due to short circuit faults. A detailed performance comparison with several contemporary, competing methods existing in the literatures for similar problems aptly demonstrates the suitability of the proposed method.

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Section: Neural Network-based Classifiersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A dual‐tree complex wavelet transform‐based approach for recognition of power system transients

Chakraborty¹,

Chatterjee

Goswami

2014

Expert Systems

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“…Cirrincione et al examine the use of a Kohonen network which acts as a diagnostic system for small and medium sized machines [5]. In contrast Kevyan et al describe a prototype signal monitoring system which utilises adaptive-resonance theory networks and a fault identification database for fault detection in a fast breeder nuclear reactor [6]. Wu Yan and Upadhyaya investigate the use of data compression methods and neural networks for eddy current inspection of steam generator tubing, using non-destructive testing [7].…”

Section: Introductionmentioning

confidence: 99%

Effectiveness of feature extraction in neural network architectures for novelty detection

Addison

Wermter²,

MacIntyre³

1999

9th International Conference on Artificial Neural Networks: ICANN '99

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This paper examines the performance of seven neural network architectures in classifying and detecting novel events contained within data collected from turbine sensors. Several different multi-layer perceptrons were built and trained using back propagation, conjugate gradient and Quasi-Newton training algorithms. In addition, Linear networks, Radial Basis Function networks, Probabilistic networks and Kohonen self organising feature maps were also built and trained, with the objective of discovering the most appropriate architecture. Because of the large input set involved in practice, feature extraction is examined to reduce the input features, the techniques considered being stepwise linear regression and a genetic algorithm. The results of these experiments have demonstrated an improvement in classification performance for multi layer perceptrons, Kohonen and probabilistic networks, using both genetic algorithms and stepwise linear regression over other architectures considered in this work. In addition, linear regression also performed better than a genetic algorithm for feature extraction. For classification problems involving a clear two class structure we consider a synthesis of stepwise linear regression with any of the architectures listed above to offer demonstrable improvements in performance for important real world tasks.

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“…Fuzzy ARTMAP has been used widely. It showed better performance than various other ANNs dealing with different problems such as, automatic analysis of electrocardiogram (Ham & Han 1996); diagnostic monitoring of nuclear plants (Keyvan et al 1993); and prediction of protein secondary structure (Mehta et al 1993).…”

Section: Supervised Art Annsmentioning

confidence: 97%

“…... Unlike traditional expert systems where knowledge is made explicit in the form of rules, neural networks genérate their own rules by learning from exemplars" (Keyvan 1993).…”

Section: Classifying Remotely Sensed Data With Annsmentioning

confidence: 99%

Design new supervised art-type artificial neural networks, and their performances for classification landsat TM images

Al-Rawi¹

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New Supervised ART ANNs with simple architectures have been developed in this study. Their architectures have been built from a single module of ART rather than a pair of them connected by a map field as all other supervised ART-type ANNs that have been reported in the literatee. Two different algorithms have been developed: Supervised ART-I and Supervised ART-II. The developed algorithms reduced the number of dynamic parameters, memory requirement, and the training time which is the major problem facing the ANNs, without altering the classification accuracy. Two simplified versión of Fuzzy ART algorithms have been developed, keeping the categorization performance as that of the original algorithm. They are Flagged Fuzzy ART and Compact Fuzzy ART. While Supervised ART-I and Supervised ART-II are general in nature that can be applied to all ART ANNs, the supervisión of Compact Fuzzy ART has been addressed in this work. The full algorithms for Supervised ART-I and Supervised ART-II have been listed. The newly developed ANNs have been applied to classify Landsat Thematic Mapper (TM) images. The performance of the systems has been tested for different dynamic parameters and different training samples. The behavior of the systems in the vigilance parameter and dynamic learning parameter space has been addressed. Their performances in the domain of the vigilance parameter and the dynamic learning parameter have been understood. Only one approach, for vigilance dynamic in all supervised ART-type ANNs, has been addressed in the literatee. Three more approaches have been developed in this study: fixed, free, and float. The performance of the developed ANNs for classification landsat TM images has been tested for all these different vigilance dynamics.to os 0.

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

Cited by 10 publications

References 7 publications

A dual‐tree complex wavelet transform‐based approach for recognition of power system transients

A dual‐tree complex wavelet transform‐based approach for recognition of power system transients

Effectiveness of feature extraction in neural network architectures for novelty detection

Design new supervised art-type artificial neural networks, and their performances for classification landsat TM images

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