Open Access

Approximation of phenol concentration using novel hybrid computational intelligence methods

International Journal of Applied Mathematics and Computer Science's Cover Image
International Journal of Applied Mathematics and Computer Science
Selected Problems of Biomedical Engineering (special section, pp. 7 - 63), Marek Kowal and Józef Korbicz (Eds.)

Antonelli, M., Ducange, P., Lazzerini, B. and Marcelloni, F. (2009). Learning concurrently partition granularities and rule bases of Mamdani fuzzy systems in a multi-objective evolutionary framework, International Journal of ApproximateReasoning 50(7): 1066-1080.10.1016/j.ijar.2009.04.004Search in Google Scholar

Aydogan, E., Karaoglan, I. and Pardalos, P. (2012). hGA: Hybrid genetic algorithm in fuzzy rule-based classification systems for high-dimensional problems, Applied Soft Computing12(2): 800-806.10.1016/j.asoc.2011.10.010Search in Google Scholar

Benrekia, F., Attari, M. and Bermak, A. (2009). FPGA implementation of a neural network classifier for gas sensor array applications, Proceedings of the 6th IEEE InternationalMulti-Conference on Systems, Signals and Devices,Djerba, Tunisia.10.1109/SSD.2009.4956804Search in Google Scholar

Cevoli, C., Cerretani, L., Gori, A., Caboni, M., Gallina, T., Toschi and Fabbri, A. (2011). Classification of Pecorino cheeses using electronic nose combined with artificial neural network and comparison with GC-MS analysis of volatile compounds, Food Chemistry 129(3): 1315-1319.10.1016/j.foodchem.2011.05.12625212373Search in Google Scholar

Chandra, R., Frean, M., Zhang, M. and Omlin, C. (2011). Encoding subcomponents in cooperative co-evolutionary recurrent neural networks, Neurocomputing74(17): 3223-3234.10.1016/j.neucom.2011.05.003Search in Google Scholar

Cheng, M.-Y., Tsai, H.-C. and Sudjono, E. (2010). Evolutionary fuzzy hybrid neural network for project cash flow control, Engineering Applications of Artificial Intelligence23(4): 604-613.10.1016/j.engappai.2009.10.003Search in Google Scholar

Cheshmehgaz, H., Haron, H., Kazemipour, F. and Desa, M. (2012). Accumulated risk of body postures in assembly line balancing problem and modeling through a multi-criteria fuzzy-genetic algorithm, Computers & IndustrialEngineering 63(2): 503-512.10.1016/j.cie.2012.03.017Search in Google Scholar

Czogała, E. and Ł˛eski, J. (2000). Fuzzy and Neuro-Fuzzy IntelligentSystems, Physica-Verlag, Springer-Verlag Com., Heidelberg/New York, NY.Search in Google Scholar

Font, J., Manrique, D. and Rios, J. (2010). Evolutionary construction and adaptation of intelligent systems, ExpertSystems with Applications 37(12): 7711-7720.10.1016/j.eswa.2010.04.070Search in Google Scholar

Ghasemi-Varnamkhasti, M., Mohtasebi, S., Siadat, M., Lozano, J., Ahmadi, H., Razavi, S. and Dicko, A. (2011). Aging fingerprint characterization of beer using electronic nose, Sensors and Actuators B: Chemical 159(1): 51-59.10.1016/j.snb.2011.06.036Search in Google Scholar

Ihokura, K. and Watson, J. (1994). The Stannic Oxide Gas Sensor:Principles and Applications, CRC Press, Boca Raton, FL. Search in Google Scholar

Lin, C.-J. and Chen, C.-H. (2011). Nonlinear system control using self-evolving neural fuzzy inference networks with reinforcement evolutionary learning, Applied Soft Computing11(8): 5463-5476.10.1016/j.asoc.2011.05.012Search in Google Scholar

Maziarz, W. and Pisarkiewicz, T. (2008). Gas sensors in a dynamic operation mode, Measurement Science and Technology19(5): 055205.10.1088/0957-0233/19/5/055205Search in Google Scholar

Maziarz, W., Potempa, P., Sutor, A. and Pisarkiewicz, T. (2003). Dynamic response of a semiconductor gas sensor analysed with the help of fuzzy logic, Thin Solid Films436(1): 127-131.10.1016/S0040-6090(03)00507-8Search in Google Scholar

M.O.S., A. (2002). Technical note, Toulouse, ND, www.alpha-mos.com.Search in Google Scholar

Nakata, S., Neya, K. and Takemura, K. (2001). Non-linear dynamic responses of a semiconductor gas sensor: Competition effect on the sensor responses to gaseous mixtures, Thin Solid Films 391(2): 293-298.10.1016/S0040-6090(01)00998-1Search in Google Scholar

Nomura, T., Fujimori, Y., Kitora, M., Matsuura, Y. and Aso, I. (1998). Battery operated semiconductor CO sensor using pulse heating method, Sensors and Actuators B52(1): 90-95.10.1016/S0925-4005(98)00261-5Search in Google Scholar

Patan, K. and Patan, M. (2011). Optimal training strategies for locally recurrent neural networks, Journal of Artificial Intelligenceand Soft Computing Research 1(22): 103-114.Search in Google Scholar

Romain, A.-C., Nicolas, J.,Wiertz, V., Maternova, J. and Andre, P. (2000). Use of a simple tin oxide sensor array to identify five malodours collected in the field, Sensors and ActuatorsB: Chemical 62(1): 73-79.10.1016/S0925-4005(99)00375-5Search in Google Scholar

Rutkowski, L. (2008). Computational Intelligence: Methods andTechniques, Springer, Berlin.10.1007/978-3-540-76288-1Search in Google Scholar

Shahlaei, M., Madadkar-Sobhani, A., Saghaie, L. and Fassihi, A. (2012). Application of an expert system based on Genetic Algorithm-Adaptive Neuro-Fuzzy Inference System (GA-ANFIS) in QSAR of cathepsin K inhibitors, Expert Systems with Applications 39(6): 6182-6191.10.1016/j.eswa.2011.11.106Search in Google Scholar

Snopok, B. and Kruglenko, I. (2002). Multisensor systems for chemical analysis: State-of-the-art in electronic nose technology and new trends in machine olfaction, Thin SolidFilms 418(1): 21-41.10.1016/S0040-6090(02)00581-3Search in Google Scholar

Su, C.-L.,Yang, S. and Huang,W. (2011). A two-stage algorithm integrating genetic algorithm and modified Newton method for neural network training in engineering systems, ExpertSystems with Applications 38(10): 12189-12194.10.1016/j.eswa.2011.03.073Search in Google Scholar

Tabor, Z. (2009). Statistical estimation of the dynamics of watershed dams, International Journal of Applied Mathematicsand Computer Science 19(2): 349-360, DOI: 10.2478/v10006-009-0030-6.10.2478/v10006-009-0030-6Search in Google Scholar

Tabor, Z. (2010). Surrogate data: A novel approach to object detection, International Journal of Applied Mathematicsand Computer Science 20(3): 545-553, DOI: 10.2478/v10006-010-0040-4.10.2478/v10006-010-0040-4Search in Google Scholar

Tadeusiewicz, R. (2010a). New Trends in Neurocybernetics, Computer Methods in Materials Science 10(1): 1-7.Search in Google Scholar

Tadeusiewicz, R. (2010b). Place and role of intelligent systems in computer science, Computer Methods in MaterialsScience 10(4): 193-206. Tadeusiewicz, R. (2011a). How intelligent should be system for image analysis? in H. Kwasnicka and L.C. Jain (Eds.), Innovations in Intelligent Image Analysis, Studies in Computational Intelligence, Vol. 339, Springer-Verlag, Berlin/Heidelberg/New York, NY.Search in Google Scholar

Tadeusiewicz, R. (2011b). Introduction to intelligent systems, in B.M. Wilamowski and J.D. Irvin (Eds.), The IndustrialElectronics Handbook-Intelligent Systems, CRC Press, Boca Raton, FL.Search in Google Scholar

Tadeusiewicz, R. and Morajda, J. (2012). Artificial intelligence methods, in P. Lula and G. Paliwoda-Pekosz (Eds.), Analysisand Data Processing Computer Methods, Cracow University of Economics Publishing House, Cracow.Search in Google Scholar

Tallon-Ballesteros, A. and Hervas-Martinez, C. (2011). A two-stage algorithm in evolutionary product unit neural networks for classification, Expert Systems with Applications38(1): 743-754.10.1016/j.eswa.2010.07.028Search in Google Scholar

Tong, D. and Schierz, A. (2011). Hybrid genetic algorithm-neural network: Feature extraction for unpreprocessed microarray data, Artificial Intelligencein Medicine 53(1): 47-56.10.1016/j.artmed.2011.06.00821775110Search in Google Scholar

Yang, S.-H. and Chen, Y.-P. (2012). An evolutionary constructive and pruning algorithm for artificial neural networks and its prediction applications, Neurocomputing86(1): 140-149.10.1016/j.neucom.2012.01.024Search in Google Scholar

Yu, H., Wang, J., Xiao, H. and Liu, M. (2009). Quality grade identification of green tea using the eigenvalues of PCA based on the E-nose signals, Sensors and Actuators B: Chemical140(2): 378-382.10.1016/j.snb.2009.05.008Search in Google Scholar

Zhang, L., Tian, F., Kadri, C., Pei, G., Li, H. and Pan, L. (2011). Gases concentration estimation using heuristics and bio-inspired optimization models for experimental chemical electronic nose, Sensors and Actuators B: Chemical160(1): 760-770. 10.1016/j.snb.2011.08.060Search in Google Scholar

ISSN:
1641-876X
Language:
English
Publication timeframe:
4 times per year
Journal Subjects:
Mathematics, Applied Mathematics