Performance of the Levenberg–Marquardt neural network training method in electronic nose applications

Kermani, Bahram G.; Schiffman, Susan S.; Nagle, H. Troy

doi:10.1016/j.snb.2005.01.008

Cited by 217 publications

(96 citation statements)

References 19 publications

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“…ANNs are computational modeling tools that have been extensively used in many disciplines to model complex problems [21]. They have been applied to E-nose data for the purpose of classification [22][23][24][25][26][27][28][29]. The trained ANN can be employed for classification of fish freshness and the identification of the day after catching.…”

Section: Neural Network Classifiersmentioning

confidence: 99%

Intelligent Fish Freshness Assessment

Hosseini

Luo

et al. 2008

Journal of Sensors

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Recommended by Subhas MukhopadhyayFish species identification and automated fish freshness assessment play important roles in fishery industry applications. This paper describes a method based on support vector machines (SVMs) to improve the performance of fish identification systems. The result is used for the assessment of fish freshness using artificial neural network (ANN). Identification of the fish species involves processing of the images of fish. The most efficient features were extracted and combined with the down-sampled version of the images to create a 1D input vector. Max-Win algorithm applied to the SVM-based classifiers has enhanced the reliability of sorting to 96.46%. The realisation of Cyranose 320 Electronic nose (E-nose), in order to evaluate the fish freshness in real-time, is experimented. Intelligent processing of the sensor patterns involves the use of a dedicated ANN for each species under study. The best estimation of freshness was provided by the most sensitive sensors. Data was collected from four selected species of fishes over a period of ten days. It was concluded that the performance can be increased using individual trained ANN for each specie. The proposed system has been successful in identifying the number of days after catching the fish with an accuracy of up to 91%.

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Section: Neural Network Classifiersmentioning

confidence: 99%

Intelligent Fish Freshness Assessment

Hosseini

Luo

et al. 2008

Journal of Sensors

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“…The e-nose does not recognize the individual odor-generating compounds, but rather provides an olfactory signature (fingerprint) of the analyzed air [10].…”

Section: Measurements and Methodsmentioning

confidence: 99%

“…create a preferential passage to the tourist area and to downtown by passing winds from West-South-West and South [10].…”

Section: Mobile E-nose Analysesmentioning

confidence: 99%

Odour Impact Assessment by Means of Dispersion Modeling, Dynamic Olfactometry and Mobile Electronic Nose around Agadir Fishing Port in Morocco

Chirmata¹,

Ichou²

2016

JEP

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Odorous emissions emitted from various sources including industrial and commercial activities have particular concerns about human health. These malodors emissions are an environmental concern that affects health status and social life of the neighbors. That requires the local authority to set up a management strategy to control this nuisance. The evaluation of odour emissions from fishing port is complex because these emissions depend on several factors such as multiple sources of odor emissions, meteorological conditions, topography and others. That imposes the use of complementary approaches to monitor odours. In this paper, the case of Agadir fishing port is studied, which is adjacent to the tourist area and residential neighborhoods and which hosts a number of points that can generate odors. To assess this odour impact, three methods are used such as dynamic olfactometry, dispersion modeling and mobile electronic nose (e-nose). The use of these three methods in a complementary manner to assess odour impacts around a fishing port allowed both the quantification of the emissions using dynamic olfactometry and the evaluation of their impact on the study area with model dispersion. The results enabled also to identify the most affected areas of the city by odor emissions and to recognize the meteorological parameters maximizing odor impact. The other goal of this work is to compare the results of the odour dispersion modeling and e-nose measurements for one year in terms of frequency of overtaking the set alert thresholds over the same period. Comparison highlights the strengths and weaknesses of both approaches. Modeling can be used predicatively but it does not take into account fugitive emissions reliably in the absence of data on these emissions, modeling based on the hourly average misjudges the odor peaks, while e-nose made it possible to obtain validated data and provides accurate, affordable and real-time odour measurement capability tacking in to account the role of human perception without being able to characterize the extent of the odor nuisance caused by each source. We conclude that these three valuation methods provide complementary information about odor nuisance and reasonable estimates of odors.

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“…This objective is attained by gradually reducing the difference between the actual and expected outputs. In this paper, supervised learning was performed with the Levenberg-Marquardt algorithm (Levenberg, 1944;Marquardt, 1963), which had been successful in training feedforward networks in various fields of research (Adeloye & Munari, 2006;Fun & Hagan, 1996;Kermani, Schiffman, & Nagle, 2005;Übeyli, 2009). The mathematical details of the algorithm might not always be consistent with neurobiological findings of mechanisms occurring at the molecular level (Kandel, 2001).…”

Section: Training Cyclementioning

confidence: 99%

The Role of Memory Templates in Experts’ Strategic Thinking

Chassy¹

2013

JPR

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There is ample evidence that experts' performance is mostly due to domain specific knowledge. Some complex memory structures, termed templates, have been theorised to underpin strategic thinking in expert chess players. A behavioural study and computer simulations have been used to test this hypothesis. The behavioural study is the first to show that experts identify strategic systems with more accuracy than novices do. A new artificial neural network model is introduced to implement visuospatial templates. The simulations indicate that templates adequately model identification of strategies in novice and expert players. Both results support the view that templates underpin strategic thinking. The findings constitute a demonstration of the dependence of strategic thinking on memory processes and open the door for a new theoretical approach to understand high-level cognition.

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Performance of the Levenberg–Marquardt neural network training method in electronic nose applications

Cited by 217 publications

References 19 publications

Intelligent Fish Freshness Assessment

Intelligent Fish Freshness Assessment

Odour Impact Assessment by Means of Dispersion Modeling, Dynamic Olfactometry and Mobile Electronic Nose around Agadir Fishing Port in Morocco

The Role of Memory Templates in Experts’ Strategic Thinking

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