Abstract
This article proposes a new method to calculate the distance between fuzzy singleton variables. It uses a measure of generalized fuzzy numbers based on the center of gravity. The fuzzy signals are transformed by applying convolution. To prove the effectiveness of this method, it is applied to a pattern recognition problem that deals with stock markets. Comparison with other classical distance measurements shows that this approach provides a consistent and reliable distance measure for the stock market scenario and can be generalized for any pattern recognition problem.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zadeh, L.: Fuzzy sets. Inf. Control 8, 338–353 (1965)
Santos, M.: Intelligent control: Applied approach. Revista Iberoamericana de Automática e Informática Industrial RIAI 8(4), 283–296 (2011)
López, V., Santos, M., Montero, J.: Fuzzy specification in real estate market decision making. Int. J. Comput. Intell. Syst. 3(1), 8–20 (2010)
Chen, S.M.: New methods for subjective mental workload assessment and fuzzy risk analysis. Cybern Syst.: Int. J. 27(5), 449–472 (1996)
Hsieh, C.H., Chen, S.H.: Similarity of generalized fuzzy numbers with graded mean integration representation. In: 8th International Proceeding on Fuzzy Systems Association World Congress, vol. 2, Taipei, pp. 551–555 (1999)
Chen, S.J., Chen. S.M.: Fuzzy risk analysis based on similarity measures of generalized fuzzy numbers, IEEE Trans. Fuzzy Syst. 11, 45–56 (2003)
Yong, D., Wenkang, S., Feng, D., Qi, L.: A new similarity measure of generalized fuzzy numbers and its application to pattern recognition. Pattern Recogn. Lett. 25, 875–883 (2004)
Chen, S.J.: New similarity measure of generalized fuzzy numbers based on geometric mean averaging operator. In: IEEE International Proceedings Conference on Fuzzy Systems, Vancouver, Canada, pp. 1879–1886 (2006)
Sridevi, B., Nadarajan, R.: Fuzzy similarity measure for generalized fuzzy numbers. Int. J. Open Probl. Comput. Sci. Math. 2(2), 240–253 (2009)
Naranjo, R., Santos, M.: Fuzzy candlesticks forecasting using pattern recognition for stock markets. In: Graña, M., López-Guede, J.M., Etxaniz, O., Herrero, Á., Quintián, H., Corchado, E. (eds.) ICEUTE/SOCO/CISIS -2016. AISC, vol. 527, pp. 323–333. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-47364-2_31
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Naranjo, R., Santos, M. (2018). New Fuzzy Singleton Distance Measurement by Convolution. In: Yin, H., Camacho, D., Novais, P., Tallón-Ballesteros, A. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2018. IDEAL 2018. Lecture Notes in Computer Science(), vol 11314. Springer, Cham. https://doi.org/10.1007/978-3-030-03493-1_84
Download citation
DOI: https://doi.org/10.1007/978-3-030-03493-1_84
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-03492-4
Online ISBN: 978-3-030-03493-1
eBook Packages: Computer ScienceComputer Science (R0)