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Warianty tytułu
Języki publikacji
Abstrakty
In this paper, we introduce an optimized method to improve the accuracy of content based image retrieval systems (CBIR). CBIR systems classify the images according to low and higher features.In our research, we improve both feature selection and classifier partition of a CBIR system. Results show great performance of our proposed algorithm.
Wydawca
Rocznik
Tom
Strony
147--153
Opis fizyczny
Bibliogr. 20 poz., rys.
Twórcy
autor
- Charles Sturt University Faculty of Business, Melbourne, Australia
Bibliografia
- [1] J.D. Boskovic and K.S. Narendra, Comparison of linear, nonlinear and neural-network-based adaptive controllers for a class of fed-batch fermentation processes, Automática, 31(6):817-840, June 1995.
- [2] S. Haykin, Neural Networks, a Comprehensive Foundation, 2-nd ed, Prentice-Hall, ISBN 0-13-273350-1: Upper Saddle River, New Jersey 07458, 1999, Section 2.13:84-89; Section 4.13:208-213.
- [3] A. Bulsari and S. Palosaari, Application of neural networks for system identification of an adsorption column, Neural Computing and Applications, 1(2):160-165, June 1993.
- [4] H. Deng and H.X. Li, Hybrid intelligence based modeling for nonlinear distributed parameter process with applications to the curing process, IEEE Transactions on Systems, Man and Cybernetics, 4(4):3506-3511, October 2003.
- [5] H. Deng, H.X. Li and G.. Chen, Spectral-approximation-based intelligent modeling for distributed thermal processes, IEEE Transactions on Control Systems Technology, 13(5):686-700, September 2005.
- [6] R. Gonzalez-Garcia, R. Rico-Martinez and I. Kevrekidis, Identification of distributed parameter systems: A neural net based approach, Computers and Chemical Engineering, 22(1):S965-S968, March 2003.
- [7] R. Padhi and S. Balakrishnan, Proper orthogonal decomposition based optimal neurocontrol synthesis of a chemical reactor process, using approximate dynamic programming, Neural Networks, 16(5-6):719-728, June 2003.
- [8] R. Padhi, S. Balakrishnan and T. Randolph, Adaptive critic based optimal neuro-control synthesis for distributed parameter systems, Automática, 37(8): 1223-1234, August 2001.
- [9] S. Pietil and H.N. Koivo, Centralized and decentralized neural network models for distributed parameter systems, Proceedings of CESA'96 IMACS Multiconference on Computational Engineering in Systems Applications, ISBN 2-9502908-9-2, Lille, France, July 1996, Gerf EC Lille, Villeneuve d'Ascq, FRANCE, 1996: 1043-1048.
- [10] I.S. Baruch, P. Georgieva, J. Barrera-Cortes and S. Feyo de Azevedo, Adaptive recurrent neural network control of biological wastewater treatment, International Journal of Intelligent Systems, Special issue on Soft Computing for Modelling, Simulation and Control of Nonlinear Dynamical Systems, O. Castillo and P. Melin, Guest Editors, ISSN 0884-8173, 20(2): 173-194, February2005.
- [11] I.S. Baruch, L.A. Hernandez, C.R. Mariaca-Gaspar and B. Nenkova, An adaptive sliding mode control with I-term using recurrent neural identifier, Cybernetics and Information Technologies, BAS, Sofia, Bulgaria, ISSN 1311-9702, 7(l):21-32, January 2007.
- [12] I.S Baruch and R. Garrido, A direct adaptive neural control scheme with integral terms, International Journal of Intelligent Systems, Special issue on Soft Computing for Modelling, Simulation and Control of Nonlinear Dynamical Systems, O. Castillo and P. Melin, Guest Editors, ISSN 0884-8173, 20(2):213-224, February 2005.
- [13] I.S. Baruch, R. Beltran-Lopez, J.L. Olivares-Guzman and J.M. Flores, A fuzzy-neural multi-model for nonlinear systems identification and control, Fuzzy Sets and Systems, Elsevier, 159(20):2650-2667, October 2008.
- [14] I.S. Baruch, R. Galvan-Guerra, C.R. Mariaca-Gaspar and O. Castillo, Fuzzy-neural control of a distributed parameter bioprocess plant, Proceedings of the IEEE International Conference on Fuzzy Systems, IEEE World Congress on Computational Intelligence, WCCI 2008, June 1-6, 2008, Hong Kong, ISBN: 978-1-4244-1819-0, ISSN 1098-7584, IEEE, 2008:2208-2215.
- [15] I.S. Baruch, R. Galvan-Guerra, C.R. Mariaca-Gaspar and P. Melin, Decentralized indirect adaptive fuzzy-neural multi-model control of a distributed parameter bioprocess plant, Proceedings of International Joint Conference on Neural Networks, IEEE World Congress on Computational Intelligence, WCCI 2008, June 1-6, 2008, Hong Kong, ISBN: 978-1-4244-1821-3, IEEE, 2008:1658-1665.
- [16] I.S. Baruch and R. Galvan-Guerra, Decentralized adaptive fuzzy-neural control of an anaerobic digestion bioprocess plant, in Proc. of the 2009 International Fuzzy Systems Association World Congress, 2009 European Society for Fuzzy Logic and Technology Conference, IFSA/ EUSFLAT 2009, 20.07-24.07.09, Lisbon, Portugal, ISBN 978-989-95079-6-8, 2009:460-465.
- [17] I.S. Baruch, C.R. Mariaca-Gaspar and J. Barrera-Cortes, Recurrent neural network identification and adaptive neural control of hydrocarbon biodegradation processes, in Xiaolin Hu and P. Balasubramaniam, Eds., Recurrent Neural Networks, I-Tech Education and Publishing KG, Vienna, Austria, ISBN 978-953-7619-08-4, 2008, Chapter 4:61-88.
- [18] I.S. Baruch and C.R. Mariaca-Gaspar, A Levenberg-Marquardt learning algorithm applied for recurrent neural identification and control of a wastewater treatment bioprocess, International Journal of Intelligent Systems, ISSN 0884-8173, 24(11):1094-1114, November 2009.
- [19] F. Aguilar-Garnica, V. Alcaraz-Gonzalez and V. Gonzalez-Alvarez, Interval observer design for an anaerobic digestion process described by a distributed parameter model, Proceedings of the Second International Meeting on Environmen¬tal Biotechnology and Engineering: 2IMEBE, ISBN 970-95106-0-6, Mexico City, Mexico: 1-16, September 2006.
- [20] B. Bialecki and G. Fairweather, Orthogonal spline collocation methods for partial differential equations, Journal of Computational and Applied Mathematics, ISSN 0377-0427, 128(l-2):55-82, March 2001
Typ dokumentu
Bibliografia
Identyfikator YADDA
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