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2017 | OriginalPaper | Chapter

7. A New Interval Arithmetic-Based Neural Network

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Abstract

The aim of this chapter is to design a new model of fuzzy nonlinear perceptron, based on alpha level sets. The new model entitled Fuzzy Nonlinear Perceptron based on Alpha Level Sets (FNPALS) Iatan, Neuro-fuzzy system for pattern recognition (in Romanian), PhD thesis, 2003, [1], Iatan and de Rijke, A new interval arithmetic based neural network, 2014, [2] differs from the other fuzzy variants of the nonlinear perceptron, where the fuzzy numbers are represented by membership values. In the case of FNPALS, the fuzzy numbers are represented through the alpha level sets.

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Footnotes
1
Leondes, C. T., Fuzzy Logic and Expert Systems Applications. San Diego, Academic Press, 1998.
 
Literature
1.
go back to reference I. Iatan. Neuro-Fuzzy Systems for Pattern Recognition (in Romanian). PhD thesis, Faculty of Electronics, Telecommunications and Information Technology- University Politehnica of Bucharest, PhD supervisor: Prof. dr. Victor Neagoe, 2003. I. Iatan. Neuro-Fuzzy Systems for Pattern Recognition (in Romanian). PhD thesis, Faculty of Electronics, Telecommunications and Information Technology- University Politehnica of Bucharest, PhD supervisor: Prof. dr. Victor Neagoe, 2003.
2.
go back to reference I. Iatan and M. de Rijke. A new interval arithmetic based neural network. (work in progress), 2014. I. Iatan and M. de Rijke. A new interval arithmetic based neural network. (work in progress), 2014.
3.
go back to reference A. Muñoz San Roque, C. Maté, J. Arroyo, and A. Sarabia. iMLP: Applying multi-layer perceptrons to interval-valued data. Neural Processing Letters, 25:157–169, 2007. A. Muñoz San Roque, C. Maté, J. Arroyo, and A. Sarabia. iMLP: Applying multi-layer perceptrons to interval-valued data. Neural Processing Letters, 25:157–169, 2007.
4.
go back to reference C. T. Leondes. Fuzzy Logic and Expert Systems Applications. San Diego, Academic Press, 1998. C. T. Leondes. Fuzzy Logic and Expert Systems Applications. San Diego, Academic Press, 1998.
5.
go back to reference M. Umano and Y. Ezawa. Execution of approximate reasoning by neural network. In Proceedings of FAN Symposium, pages 267–273, 1991. M. Umano and Y. Ezawa. Execution of approximate reasoning by neural network. In Proceedings of FAN Symposium, pages 267–273, 1991.
6.
go back to reference K. Uehara and M. Fujise. Learning of fuzzy inference criteria with artificial neural network. In Proc. 1st Int. Conf. on Fuzzy Logic and Neural Networks, pages 193–198, 1990. K. Uehara and M. Fujise. Learning of fuzzy inference criteria with artificial neural network. In Proc. 1st Int. Conf. on Fuzzy Logic and Neural Networks, pages 193–198, 1990.
Metadata
Title
A New Interval Arithmetic-Based Neural Network
Author
Iuliana F. Iatan
Copyright Year
2017
DOI
https://doi.org/10.1007/978-3-319-43871-9_7

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