CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
Implementation of Feature Selection Using Correlation Matrix in Python

Ahmad Farhan AlShammari
Random Articles
Reseach Article

Study and Software Implementation of Variational Bayesian Approach to Mixed Deterministic/Stochastic Fuzzy Models

by Sukhvir Singh, Yogesh Mohan, Kishori Lal Bansal
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 73 - Number 4
Year of Publication: 2013
Authors: Sukhvir Singh, Yogesh Mohan, Kishori Lal Bansal
10.5120/12727-9571

Sukhvir Singh, Yogesh Mohan, Kishori Lal Bansal . Study and Software Implementation of Variational Bayesian Approach to Mixed Deterministic/Stochastic Fuzzy Models. International Journal of Computer Applications. 73, 4 ( July 2013), 8-17. DOI=10.5120/12727-9571

@article{ 10.5120/12727-9571,
author = { Sukhvir Singh, Yogesh Mohan, Kishori Lal Bansal },
title = { Study and Software Implementation of Variational Bayesian Approach to Mixed Deterministic/Stochastic Fuzzy Models },
journal = { International Journal of Computer Applications },
issue_date = { July 2013 },
volume = { 73 },
number = { 4 },
month = { July },
year = { 2013 },
issn = { 0975-8887 },
pages = { 8-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume73/number4/12727-9571/ },
doi = { 10.5120/12727-9571 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:39:09.127808+05:30
%A Sukhvir Singh
%A Yogesh Mohan
%A Kishori Lal Bansal
%T Study and Software Implementation of Variational Bayesian Approach to Mixed Deterministic/Stochastic Fuzzy Models
%J International Journal of Computer Applications
%@ 0975-8887
%V 73
%N 4
%P 8-17
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The study explains a new emerging methodology Variational Bayesian Inference (VB) to structure optimization of Fuzzy System (Takagi-Sugeno fuzzy system). Recently, the study of (Kumar et al. 2010 a) introduced a mixed Takagi-Sugeno fuzzy filter whose antecedents are deterministic while the consequents are random variables. The parameters of fuzzy filters are inferred under VB framework. The objective of this study is to show how computational intelligence based model contribute to the methodology of constructing models of software processes and products. The study provides detailed software implementation of Variation Bayesian approach to mixed deterministic/stochastic fuzzy models and also helps in software developments of some computational optimization algorithms based on Variational Bayesian approach. The developed MATLAB software can be used in the field of image processing, signal processing, pattern recognition, machine learning.

References
  1. L. A. Zadeh, Fuzzy Sets, Information and Control, 1965.
  2. L. A. Zadeh, "Outline of a New Approach to the Analysis of Complex Syestems and Decision Processes," IEEE Trans. Syst. , Man, Cybern. , vol. SMC-3, no. 1, pp. 28-44 Jan. 1973.
  3. T. TAKAGI and M. SUGENO, "Fuzzy identification of system and its application to modelling and control," IEEE Trans. Syst. , Man, Cybern. , vol. 15, no. 1, pp. 116-132, 1985.
  4. L. A. Zadeh, "The role of fuzzy logic in the management of uncertainty in expert systems," Fuzzy Sets Syst. , vol. 11, pp. 199-227, 1983.
  5. M. Kumar, D. Arndt, S. Kreuzfeld, K. Thurow, N. Stoll, and R. Stoll, "Fuzzy techniques for subjective workload score modelling under uncertainties," IEEE Trans. Syst. , Man, Cybern. B, Cybern. , vol. 38, no. 6, pp. 1449-1464, Dec. 2008.
  6. M. Kumar, M. Weippert, R. Vilbrandt, S. Kreuzfeld, and R. Stoll, "Fuzzy evaluation of heart rate signals for mental stress assessment," IEEE Transaction on Fuzzy Systems, vol. 15, no. 5, pp. 791-808, 2007
  7. J. -S. R. jang, "ANFIS: Adaptive-network-based fuzzy inference systems," IEEE Trans. Syst. , Man, Cybern. , vol. 23, no. 3, pp. 665-685, May 1993.
  8. L. Ljung, System Identification, Theory for the User. New Jersey: Prentice-Hall, 1987.
  9. M. Kumar, N. Stoll, and R. Stoll, "Variational Bayes for a Mixed Stochastic/Determinstic Fuzzy Filter," IEEE Transactions on Fuzzy Systems, vol. 18, no. 4, pp. 787-801, 2010.
  10. M. Kumar, M. Weippert, N. Stoll, and R. Stoll, "A mixture of fuzzy filters applied to the analysis of heartbeat intervals," Fuzzy Optim. Decis. Making, vol. 9, no. 4, pp. 383-412, 2010.
  11. H. Attias, "A Variational Bayesian framework for graphical models," In Advances In Neural Information Processing Systems 12. Cambridge, MA: MIT Press, 2000, pp. 209-215.
  12. H. Lappalainen and J. W. Miskin, "Ensemble learning," in Advances in independent component Analysis, M. Girolami, Ed. New York: Springer-Verlag, 2000.
  13. MATLAB-The Language of Technical Computing [Online]. Available:http://www. mathworks. com/products/matlab/
  14. Optimization Toolbox—MATLAB [Online]. Available: http://www. mathworks. com/products/optimization/
  15. C. C. Chuang, S. F. Su, and S. S. Chen, "Robust TSK fuzzy modeling for function approximation with outliers," IEEE Trans. Fuzzy Syst. , vol. 9, no. 6, pp. 810-821, Dec. 2001.
  16. J. M. Leski, "TSK-fuzzy modeling based on -intensive learning," IEEE Trans. Fuzzy Syst. , vol. 13, no. 2, pp. 181-193, Apr. 2005.
Index Terms

Computer Science
Information Sciences

Keywords

Fuzzy Modeling Takagi-Sugeno Fuzzy Model Variational Bayesian Inference Stochastic Model Matlab Software

Powered by PhDFocusTM