Computational Intelligence: Retrospection and Future

Witold Pedrycz

doi:10.20965/jaciii.2017.p0009

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JACIII Vol.21 No.1 pp. 9-12

doi: 10.20965/jaciii.2017.p0009

(2017)

Invited Paper:

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Computational Intelligence: Retrospection and Future

Witold Pedrycz

Department of Electrical & Computer Engineering, University of Alberta
Edmonton, AB, T6R 2V4, Canada

Received:

October 13, 2016

Accepted:

December 5, 2016

Published:

January 20, 2017

Keywords:

computational intelligence, synergy, neural networks, fuzzy sets, granular computing

Abstract

This study is aimed at a brief, carefully focused retrospective view at the Computational Intelligence – a paradigm supporting the analysis and synthesis of intelligent systems. We stress the reason behind the emergence of this discipline and identify its main features. We highlight the synergistic aspects of Computational Intelligence arising from an interaction and collaboration of fuzzy sets, neural networks, and evolutionary optimization. Some promising directions of future fundamental and applied research are also identified.

Cite this article as:

W. Pedrycz, “Computational Intelligence: Retrospection and Future,” J. Adv. Comput. Intell. Intell. Inform., Vol.21 No.1, pp. 9-12, 2017.

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References

[1] J. C. Bezdek,“ On the relationship between neural networks, pattern recognition and intelligence,” Int. J. of Approximate Reasoning, Vol.6, pp. 85-107, 1992.
[2] W. Pedrycz, “Computational Intelligence: An Introduction,” CRC Press, Boca Raton, 1997.
[3] J. Kacprzyk and W. Pedrycz, “Handbook of Computational Intelligence,” Springer, Berlin, 2015.
[4] Y. Shi and R. C. Eberhart, “Empirical study of particle swarm optimization,” Proc. Congr. Evol. Computations, pp. 1945-1950, 1999.
[5] R. Storn and K. Price, “Differential Evolution – a simple and efficient heuristic for global optimization over continuous spaces,” J. of Global Optimization, Vol.11, pp. 341-359, 1997.
[6] R. Alcala, M. J. Gacto, and F. Herrera, “A fast and scalable multiobjective genetic fuzzy system for linguistic fuzzy modeling in high-dimensional regression problems,” IEEE Trans. Fuzzy Syst., Vol.19, pp. 666-681, 2011.
[7] W. Pedrycz and F. Gomide, “Fuzzy Systems Engineering: Toward Human-Centric Computing,” J. Wiley, Hoboken, NJ, 2007.
[8] W. Pedrycz, “Knowledge-Based Clustering: From Data to Information Granules,” J. Wiley, Hoboken, NJ, 2005.
[9] L.A. Zadeh, “Toward a generalized theory of uncertainty (GTU) – an outline,” Information Sciences, Vol.172, pp. 1-40, 2005.
[10] L. A. Zadeh, “A note on Z-numbers,” Information Sciences, Vol.181, pp. 2923-2932, 2011.
[11] A. Bargiela and W. Pedrycz, “Granular Computing: An Introduction,” Kluwer Academic Publishers, Dordrecht, 2003.
[12] W. Pedrycz, “Granular Computing: Analysis and Design of Intelligent Systems,” CRC Press/Francis Taylor, Boca Raton, 2013.
[13] L. A. Zadeh, “Towards a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic,” Fuzzy Sets and Systems, Vol.90, pp. 111-117, 1997.
[14] W. Pedrycz, “Shadowed sets: representing and processing fuzzy sets,” IEEE Trans. on Systems, Man, and Cybernetics, Part B, Vol.28, pp. 103-109, 1998.
[15] Z. Pawlak, “Rough sets,” Int. J. of Information and Computer Science, Vol.11, pp. 341-356, 1982.
[16] Z. Pawlak, “Rough Sets: Theoretical Aspects of Reasoning About Data,” Kluwer Academic Publishers, Dordecht, 1991.
[17] C. Hwang and F. C. H Rhee, “Uncertain fuzzy clustering: Interval Type-2 Fuzzy approach to C-Means,” IEEE Trans. on Fuzzy Systems, Vol.15, pp. 107-120, 2007.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] J. C. Bezdek,“ On the relationship between neural networks, pattern recognition and intelligence,” Int. J. of Approximate Reasoning, Vol.6, pp. 85-107, 1992.

[2] [2] W. Pedrycz, “Computational Intelligence: An Introduction,” CRC Press, Boca Raton, 1997.

[3] [3] J. Kacprzyk and W. Pedrycz, “Handbook of Computational Intelligence,” Springer, Berlin, 2015.

[4] [4] Y. Shi and R. C. Eberhart, “Empirical study of particle swarm optimization,” Proc. Congr. Evol. Computations, pp. 1945-1950, 1999.

[5] [5] R. Storn and K. Price, “Differential Evolution – a simple and efficient heuristic for global optimization over continuous spaces,” J. of Global Optimization, Vol.11, pp. 341-359, 1997.

[6] [6] R. Alcala, M. J. Gacto, and F. Herrera, “A fast and scalable multiobjective genetic fuzzy system for linguistic fuzzy modeling in high-dimensional regression problems,” IEEE Trans. Fuzzy Syst., Vol.19, pp. 666-681, 2011.

[7] [7] W. Pedrycz and F. Gomide, “Fuzzy Systems Engineering: Toward Human-Centric Computing,” J. Wiley, Hoboken, NJ, 2007.

[8] [8] W. Pedrycz, “Knowledge-Based Clustering: From Data to Information Granules,” J. Wiley, Hoboken, NJ, 2005.

[9] [9] L.A. Zadeh, “Toward a generalized theory of uncertainty (GTU) – an outline,” Information Sciences, Vol.172, pp. 1-40, 2005.

[10] [10] L. A. Zadeh, “A note on Z-numbers,” Information Sciences, Vol.181, pp. 2923-2932, 2011.

[11] [11] A. Bargiela and W. Pedrycz, “Granular Computing: An Introduction,” Kluwer Academic Publishers, Dordrecht, 2003.

[12] [12] W. Pedrycz, “Granular Computing: Analysis and Design of Intelligent Systems,” CRC Press/Francis Taylor, Boca Raton, 2013.

[13] [13] L. A. Zadeh, “Towards a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic,” Fuzzy Sets and Systems, Vol.90, pp. 111-117, 1997.

[14] [14] W. Pedrycz, “Shadowed sets: representing and processing fuzzy sets,” IEEE Trans. on Systems, Man, and Cybernetics, Part B, Vol.28, pp. 103-109, 1998.

[15] [15] Z. Pawlak, “Rough sets,” Int. J. of Information and Computer Science, Vol.11, pp. 341-356, 1982.

[16] [16] Z. Pawlak, “Rough Sets: Theoretical Aspects of Reasoning About Data,” Kluwer Academic Publishers, Dordecht, 1991.

[17] [17] C. Hwang and F. C. H Rhee, “Uncertain fuzzy clustering: Interval Type-2 Fuzzy approach to C-Means,” IEEE Trans. on Fuzzy Systems, Vol.15, pp. 107-120, 2007.