Drift counteraction with multiple self-organising maps for an electronic nose

Zuppa, M.; Distante, Cosimo; Siciliano, Pietro; Persaud, Krishna

doi:10.1016/j.snb.2003.10.029

Cited by 97 publications

(41 citation statements)

References 17 publications

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“…Adaptive neural networks are an active area of research and promise even more sophisticated neural networks that can automatically compensate for the drift effect [13].…”

Section: Test Phase Readjustment For Sensor Drift Compensationmentioning

confidence: 99%

“…Based on minimization of ED, the authors in [18] proposed using the ED criterion to train the adaptive system in order to match two PDFs from different output patterns, and successively applied it to classification problems with a real biomedical data set [13]. As a similar approach, [14] applied an ED minimization method to compensate for sensor drift in which the RBFN weights are readjusted even during the test phase, but only with the use of output data samples that were obtained during the training phase.…”

Section: Test Phase Readjustment For Sensor Drift Compensationmentioning

confidence: 99%

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Signal Processing Techniques Based on Adaptive Radial Basis Function Networks for Chemical Sensor Arrays

Byun¹

2016

Journal of Sensor Science and Technology

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The use of a chemical sensor array can help discriminate between chemicals when comparing one sample with another. The ability to classify pattern characteristics from relatively small pieces of information has led to growing interest in methods of sensor recognition. A variety of pattern recognition algorithms, including the adaptive radial basis function network (RBFN), may be applicable to gas and/ or odor classification. In this paper, we provide a broad review of approaches for various types of gas and/or odor identification techniques based on RBFN and drift compensation techniques caused by sensor poisoning and aging.

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“…Adaptive neural networks are an active area of research and promise even more sophisticated neural networks that can automatically compensate for the drift effect [13].…”

Section: Test Phase Readjustment For Sensor Drift Compensationmentioning

confidence: 99%

Section: Test Phase Readjustment For Sensor Drift Compensationmentioning

confidence: 99%

Signal Processing Techniques Based on Adaptive Radial Basis Function Networks for Chemical Sensor Arrays

Byun¹

2016

Journal of Sensor Science and Technology

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“…In other words, if a cluster moves to a new position, it is not obvious that all the feature points belonging to that cluster will be updated. This behaviour could give rise to confusion, since in the middle of a cluster there could be a feature point that belongs to another cluster and it has not been represented correctly for a very long time [4,14].…”

Section: M-fcm Based Drift Correctionmentioning

confidence: 99%

“…Successful development of this type of framework could potentially enhance the drift correction capabilities which in turn will enhance the reliability of the sensor network and reduce the maintenance worries. In this paper novel hybrid sensor informatics architecture based on Discrete Wavelet Transform (DWT) and multiple Fuzzy C Means clustering (m-FCM) has been investigated and proposed to estimate dynamic sensor drift and potential drift correction [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Environmental Sensor Drift Correction using Wavelet-mFCM-FIS Architecture: An Unsupervised Machine Learning Approach

Dutta¹

2013

IJCA

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A novel hybrid sensor informatics architecture based on Discrete Wavelet Transform (DWT), Fuzzy logic based clustering (FCM) and Fuzzy Rule based Inference system (FIS) has been investigated and proposed to estimate dynamic generic sensor drift during physical sampling. DWT has been used for sensor pre-processing; data dimension reduction and feature extraction from sensor time series, where as DWT-FCM based approach has been used to estimate the cumulative drift in our sensory system. An intelligent generalized algorithm using multiple FCM maps (m-FCM) has been proposed to implement the drift correction on any future unknown data sets. In the next stage a newly proposed DWT-FCM-FIS architecture has been tested on multisensory environmental data sets covering three consecutive years (2009 -11) to predict probable month (environmental time of the year) with up to 93% accuracy. This novel hybrid data processing architecture have been implemented and tested upon a real time estuary sensory platform of temperature, conductivity and salinity sensors that have been deployed to monitor the Derwent Estuary in Hobart, Australia. This newly proposed approach could be an adaptive solution to tackle drifts in the sensor networks and improve overall monitoring quality.

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“…On the other hand, signal processing methods for drift counteraction are based on different approaches: univariate [13,14] or multivariate [15][16][17], linear [18,19] or non-linear [15][16][17], adaptive [15,16] or not, based in reference samples [14] or based in component removal [18,19]. Univariate corrections include basic baseline corrections [13], or more complex per-sensor correction by means of a calibration sample [14].…”

Section: Introductionmentioning

confidence: 99%

Drift compensation of gas sensor array data by Orthogonal Signal Correction

Padilla

Perera

Montoliu

et al. 2010

Chemometrics and Intelligent Laboratory Systems

210

103

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Drift is an important issue that impairs the reliability of gas sensing systems. Sensor aging, memory effects and environmental disturbances produce shifts in sensor responses that make initial statistical models for gas or odor recognition useless after a relatively short period (typically few weeks). Frequent recalibrations are needed to preserve system accuracy. However, when recalibrations involve numerous samples they become expensive and laborious. An interesting and lower cost alternative is drift counteraction by signal processing techniques. Orthogonal Signal Correction (OSC) is proposed for drift compensation in chemical sensor arrays. The performance of OSC is also compared with Component Correction (CC). A simple classification algorithm has been employed for assessing the performance of the algorithms on a dataset composed by measurements of three analytes using an array of seventeen conductive polymer gas sensors over a ten month period.

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Drift counteraction with multiple self-organising maps for an electronic nose

Cited by 97 publications

References 17 publications

Signal Processing Techniques Based on Adaptive Radial Basis Function Networks for Chemical Sensor Arrays

Signal Processing Techniques Based on Adaptive Radial Basis Function Networks for Chemical Sensor Arrays

Environmental Sensor Drift Correction using Wavelet-mFCM-FIS Architecture: An Unsupervised Machine Learning Approach

Drift compensation of gas sensor array data by Orthogonal Signal Correction

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