1 |
J. Moody and C. Darken, "Fast learning in networks of locally-tuned processing units", Neural computation, Vol. 1, pp. 281-294, 1989.
DOI
|
2 |
M. Musavi, W. Ahmed, K. Chan, K. Faris, and D. Hummels, "On the training of radial basis function classifiers", Neural Networks, Vol. 5, pp. 595-603, 1992.
DOI
|
3 |
T. Kohonen, "Self-Organization and Associative Memory", Springer-Verlag, 3rd ed., pp. 199-202, 1989.
|
4 |
K. C. Persaud and H-G. Byun, "Classification of complex odours using conducting polymer arrays and neural networks", Industrial applications of neural networks, Eds. Fogelman Soulie, World Scientific, Singapore, New Jersey, pp. 85-90, 1998.
|
5 |
D-H. Lee, J. S. Payne, H-G. Byun, and K. C. Persaud, "Application of radial basis neural networks to odour sensing using a broad specificity array of conducting polymers", Lecture Notes in Computer Science (Eds. C. Von der Malsburg, W. von Seelen, J. C. Vorbroggen, B. Sendhoff), Vol. 1112, pp. 299-304, 1996.
|
6 |
H. Byun, N. Kim, K. Persaud, J. Huh, and D. Lee, "Application of adaptive RBF networks to odor classification using conducting polymer sensor array", Proceedings of ISOEN'2000, Washington D.C., U.S.A., Brighton, pp. 121-126, 2000.
|
7 |
H. Byun, N. Kim, K. Persaud, J. Huh, and D. Lee, "Application of adaptive RBF network for odour classification under drift effect using conducting polymer sensor array", Proceedings of Electrochemical Society, pp. 176-180, 2001.
|
8 |
I. Cha, and S. Kassam, "Interference cancellation using radial basis function networks", Signal Processing, Vol. 47, pp. 247-268, 1995.
DOI
|
9 |
K. Kwon, N. Kim, H. Byun, and K. Persaud, "On training neural network algorithms for odor identification for future multimedia communication systems", Proceedings of 2006 IEEE International Conference on Multimedia, pp. 1309-1312, 2006.
|
10 |
N. Kim, H. Byun, and K. Persaud, "Normalization approach to the stochastic gradient radial basis function network algorithm for odor sensing systems", Sensors and Actuators B, pp. 407-412, 2007.
|
11 |
P Pelosi and K. Persaud, "Toward an artificial nose", Sensors and Sensory Systems for Advanced Robots, NATO ASI Series, Vol. F43, pp. 49-70, 1998.
|
12 |
J. W. Gardner et al., Electronic Noses Principles and Applications, Oxford University Press, 1999.
|
13 |
M. Zuppa et al., "Drift counteraction with multiple selforganising maps for an electronic nose", Sensors and Actuators B, pp. 305-317, 2004.
|
14 |
D. Erdogmus, Y. Rao, and J. Principe, "Supervised training of adaptive systems with partially labeled data", Proceedings of the International Conference on ASSP, pp. v321-v324, 2005.
|
15 |
N. Kim, H. Byun, K. Persaud, and J. Huh, "Sensor drift compensation algorithm based on PDF distance minimization", Proceedings of ISOEN 2009, Vol. 1137, pp. 554-557, 2009.
|
16 |
J. Principe, D. Xu, and J. Fisher, "Information Theoretic Learning"' in: S. Haykin, Unsupervised Adaptive Filtering, New York, Wiley, pp. 265-319, 2000.
|
17 |
D. Erdogmus and J. Principe, "An Entropy minimization algorithm for supervised training of nonlinear systems", IEEE Trans. Signal Processing, Vol. 50, pp. 1780-1786, 2002.
DOI
|
18 |
K. Jeong, J. Xu, D. Erdogmus, and J. Principe, "A new classifier based on information theoretic learning with unlabeled data", Neural Networks, Vol. 18, pp. 719-726, 2005.
DOI
|
19 |
N. Kim, H. Byun, and K. Persaud, "Novel signal processing techniques based on PDF information for sensor-drift compensation", Sensor Letters, Vol. 9, No. 1, pp. 439-443, 2011.
DOI
|
20 |
N. Kim, H. Byun, K. Kwon, K. Persaud, and O.J. Lim, "Unsupervised adjustment of centers in RBF networks for sensor drift compensation", Proceedings of IMCS 2012, pp. 1802-1804, 2012.
|
21 |
I. Santamaria, P. Pokharel, and J. Principe, "Generalized correlation function: Definition, properties, and application to blind equalization", IEEE Trans. Signal Processing, Vol. 54, pp. 2187-2197, 2006.
DOI
|
22 |
N. Kim, H. Byun, and K. Persaud, "A new compensation method for sensor-drift effect based on the cross-correntropy concept", Sensor Letters, Vol. 9, No. 2, pp. 710-713, 2011.
DOI
|
23 |
V. Vapnik, The Nature of Statistical Learning Theory, New York, Springer Verlag, 1995.
|
24 |
W. Liu, P. Pokharel, and J. Principe, "Correntropy: Properties and applications in non-Gaussian signal processing", IEEE Trans. on Signal Processing, Vol. 55, No. 6, pp. 2187-2198, 2007.
|