View source for Issue:Opportunities for application of the intercriteria analysis method to neural network preprocessing procedures

[[Category:Publications on intuitionistic fuzzy sets|{{PAGENAME}}]]
[[Category:Publications in Notes on IFS|{{PAGENAME}}]]
[[Category:Publications in 2015 year|{{PAGENAME}}]]
{{issue/title
 | title           = Opportunities for application of the intercriteria analysis method to neural network preprocessing procedures
 | shortcut        = nifs/21/4/143-152
}}
{{issue/author
 | author          = Sotir Sotirov
 | institution     = Laboratory of Intelligent Systems, University “Prof. Dr. Assen Zlatarov”
 | address         = 1 “Prof. Yakimov” Blvd., Burgas 8010, Bulgaria
 | email-before-at = ssotirov
 | email-after-at  = btu.bg
}}
{{issue/data
 | conference      = 
 | issue           = [[Notes on Intuitionistic Fuzzy Sets/21/4|"Notes on IFS", Volume 21, 2015, Number 4]], pages 143–152
 | file            = NIFS-21-4-143-152.pdf
 | format          = PDF
 | size            = 99
 | abstract        = The artificial neural networks (ANN) are a tool that can be used for object recognition and identification. However, there are certain limits when we may use ANN, and the number of the neurons is one of the major parameters during the implementation of the ANN. On the other hand, the bigger number of neurons slows down the learning process. In our paper, we use a method for removing the number of the neurons without reducing the error between the target value and the real value obtained at the output of the ANN’s output. The method uses the recently proposed approach of InterCriteria Analysis, based on index matrices and intuitionistic fuzzy sets, which aims to detect possible correlations between pairs of criteria. In this paper we use the data from 11 criteria of crude oil measurements.
 | keywords        = Intercriteria analysis, Intuitionistic fuzzy sets, Neural networks, Crude oil.
 | ams             = 03E72.
 | references      = 
# Atanassov K. (1991) Generalized Nets. World Scientific, Singapore.
# Atanassov K., D. Mavrov & V. Atanassova (2014) Inter¬Criteria decision making. A new approach for multicriteria decision making, based on index matrices and intuitionistic fuzzy sets. Issues in IFS and GN, 11, 1–7.
# Atanassov K. (1983) Intuitionistic fuzzy sets, Proc. of VII ITKR's Session, Sofia, June (in Bulgarian).
# Atanassov K. (1986) Intuitionistic fuzzy sets. Fuzzy Sets and Systems. 20(1) 87–96.
# Atanassov K. (1999) Intuitionistic Fuzzy Sets. Springer, Heidelberg.
# Atanassov, K. (2012) On Intuitionistic Fuzzy Sets Theory. Springer, Berlin.
# Atanassova, V., D. Mavrov, L. Doukovska & K. Atanassov (2014) Discussion on the threshold values in the InterCriteria Decision Making approach. Notes on Intuitionistic Fuzzy Sets, 20(2), 94–99. 
# Bellis, S., K. M. Razeeb, C. Saha, K. Delaney, C. O'Mathuna, A. Pounds-Cornish, G. de Souza, M. Colley, H. Hagras, G. Clarke, V. Callaghan, C. Argyropoulos, C. Karistianos, & G. Nikiforidis (2004) FPGA Implementation of Spiking Neural Networks – An Initial Step towards Building Tangible Collaborative Autonomous Agents, Proc. of FPT’04, Int. Conf. on Field-Programmable Technology, Brisbane, Australia, 449–452. 
# Hagan, M. H. Demuth & M. Beale (1996) Neural Network Design, Boston, MA: PWS Publ. 
# Haykin, S. (1994) Neural Networks: A Comprehensive Foundation NY: Macmillan.
# Himavathi, S., D. Anitha & A. Muthuramalingam (2007) Feedforward Neural Network Implementation in FPGA Using Layer Multiplexing for Effective Resource Utilization, IEEE Transactions on Neural Networks, 18(3), 880–888.
# Karantonis, D. M., M. R. Narayanan, M. Mathie, N. H. Lovell & B. G. Celler (2006), Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring, IEEE Trans. Inform. Technol. Biomed., 10(1), 156–167.
# Meissner, M., M. Schmuker, & G. Schneider (2006) Optimized Particle Swarm Optim-ization (OPSO) and its application to artificial neural network training. BMC Bioinformatics 7(1), 125.
# Rumelhart, D., G. Hinton, & R. Williams (1986) Training representation by back-propagation errors, Nature, 323, 533–536.
# Zadeh, L. A. (1965) Fuzzy Sets. Information and Control, 8, 333–353. 
# Zwe-Lee Gaing (2004) Wavelet-based neural network for power disturbance recognition and classification, IEEE Transactions on Power Delivery, 19(4), 1560–1568.
# Mustafa, Z., Surchev, S., Milina, R., & Sotirov, S. (2015). A contribution to the recog-nition of biodiesel fuels according to their fatty acid methyl esters profiles by the artificial neural networks. Petroleum & Coal, 57(1), 40–47.
# Sotirov S., V. Atanassova, E. Sotirova, V. Bureva & D. Mavrov (2015) Application of the intuitionistic fuzzy intercriteria analysis method to a neural network preprocessing procedure, 16th Congress of IFSA, 9th Conf. of EUSFLAT, Atlantis Press, 1559–1564.

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