Signal Processing Techniques Based on Adaptive Radial Basis Function Networks for Chemical Sensor Arrays
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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
Abstract
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
참고문헌 (24)
- J. Moody, "Fast learning in networks of locally-tuned processing units", Neural computation, 1, pp.281~294 (1989)
- M. Musavi, "On the training of radial basis function classifiers", Neural Networks, 5, pp.595~603 (1992)
- T. Kohonen, "Self-Organization and Associative Memory", pp.199~202 (1989), Springer-Verlag
- K. C. Persaud, "Industrial applications of neural networks", pp.85~90 (1998), World Scientific
- D-H. Lee, "Application of radial basis neural networks to odour sensing using a broad specificity array of conducting polymers", Lecture Notes in Computer Science, 1112, pp.299~304 (1996)
- H. Byun, "Application of adaptive RBF networks to odor classification using conducting polymer sensor array", Proceedings of ISOEN’2000, pp.121~126 (2000)
- H. Byun, "Application of adaptive RBF network for odour classification under drift effect using conducting polymer sensor array", Proceedings of Electrochemical Society, pp.176~180 (2001)
- I. Cha, "Interference cancellation using radial basis function networks", Signal Processing, 47, pp.247~268 (1995)
- K. Kwon, "On training neural network algorithms for odor identification for future multimedia communication systems", Proceedings of 2006IEEE International Conference on Multimedia, pp.1309~1312 (2006)
- N. Kim, "Normalization approach to the stochastic gradient radial basis function network algorithm for odor sensing systems", Sensors and Actuators B, pp.407~412 (2007)
- P Pelosi, "Toward an artificial nose", Sensors and Sensory Systems for Advanced Robots, NATO ASI Series, F43, pp.49~70 (1998)
- J. W. Gardner, "Electronic Noses Principles and Applications" (1999), Oxford University Press
- M. Zuppa, "Drift counteraction with multiple selforganising maps for an electronic nose", Sensors and Actuators B, pp.305~317 (2004)
- N. Kim, "Sensor drift compensation algorithm based on PDF distance minimization", Proceedings of ISOEN 2009, 1137, pp.554~557 (2009)
- J. Principe, "Unsupervised Adaptive Filtering", pp.265~319 (2000), Wiley
- D. Erdogmus, "An Entropy minimization algorithm for supervised training of nonlinear systems", IEEE Trans. Signal Processing, 50, pp.1780~1786 (2002)
- D. Erdogmus, "Supervised training of adaptive systems with partially labeled data", Proceedings of the International Conference on ASSP, pp.v321~v324 (2005)
- K. Jeong, "A new classifier based on information theoretic learning with unlabeled data", Neural Networks, 18, pp.719~726 (2005)
- N. Kim, "Novel signal processing techniques based on PDF information for sensor-drift compensation", Sensor Letters, 9(1), pp.439~443 (2011)
- N. Kim, "Unsupervised adjustment of centers in RBF networks for sensor drift compensation", Proceedings of IMCS 2012, pp.1802~1804 (2012)
- I. Santamaria, "Generalized correlation function : Definition, properties, and application to blind equalization", IEEE Trans. Signal Processing, 54, pp.2187~2197 (2006)
- N. Kim, "A new compensation method for sensor-drift effect based on the cross-correntropy concept", Sensor Letters, 9(2), pp.710~713 (2011)
- V. Vapnik, "The Nature of Statistical Learning Theory" (1995), Springer Verlag
- W. Liu, "Correntropy : Properties and applications in non-Gaussian signal processing", IEEE Trans. on Signal Processing, 55(6), pp.2187~2198 (2007)