The Prediction of Air Pollution by Using Neuro-fuzzy GMDH
-
2258
Downloads
-
3048
Views
Authors
A. Yousefpour
- Islamic Azad university of Qaemshahr Branch
Z. Ahmadpour
- Islamic Azad University of Ayatollah Amoli Branch
Abstract
Air pollution is one of today modern life phenomena. It is resulted to create round-the-clock human begin activities. Control of environment pollution is complicated scientific process that enters policy, economy, technology and sociology. GMDH (Group Method of Data Handling) has been used for the identification of a mathematical model that has many input variables but limited data needs by using a hierarchical structure. This paper proposes a Neuro-fuzzy GMDH model, adopting Gaussian radial basis functions (GRBF) as partial descriptions of GMDH. GRBF is reinterpreted as both a simplified fuzzy reasoning model and as a three-layered neural network. In this paper, is used Neuro-fuzzy GMDH algorithm for predicting air pollution data and then were compared the results of predicting air pollution data by using Neuro-fuzzy GMDH and Multi Layer Perceptron (MLP).
Share and Cite
ISRP Style
A. Yousefpour, Z. Ahmadpour, The Prediction of Air Pollution by Using Neuro-fuzzy GMDH, Journal of Mathematics and Computer Science, 2 (2011), no. 3, 488--494
AMA Style
Yousefpour A., Ahmadpour Z., The Prediction of Air Pollution by Using Neuro-fuzzy GMDH. J Math Comput SCI-JM. (2011); 2(3):488--494
Chicago/Turabian Style
Yousefpour, A., Ahmadpour, Z.. "The Prediction of Air Pollution by Using Neuro-fuzzy GMDH." Journal of Mathematics and Computer Science, 2, no. 3 (2011): 488--494
Keywords
- Air pollution data
- Neuro-fuzzy GMDH
- Gaussian radial basis functions
- gradient descent.
MSC
References
-
[1]
H. Ichihashi, N. Harada, K. Nagasa, Selection of the optimum Number of Hidden Layers in Neuro-Fuzzy GMDH, International Joint Conference of the Fourth IEEE International Conference on Fuzzy Systems and The Second International Fuzzy Engineering Symposium (Vol. 3, pp. 1519--1526), Japan (1995)
-
[2]
T. Ohtani, H. Ichihashi, T. Miyoshi, K. Nagasaka, Y. Kanaumi, Structural Learning of Neurofuzzy GMDH with Minkonski Norm, Second International Conference. Knowledge-Based Intelligent Electronic Systems, Japan (1998)
-
[3]
K. Miyagishi, M. Ohsaka, Temperature prediction from regional spectral model by neurofuzzy GMDH, Proc. of The Second Asia-Pacific Conference on Industrial Engineering and Management Systems (pp. 705-708), Japan (2002)
-
[4]
Z. Xiao-mei, S. Zhi-huan, L. Ping, A Novel NF-GMDH-IFL and Its Application to Identification and Prediction of Nonlinear System, IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering, China (2002)
-
[5]
Y. Jin, B. Sendhoff, Extracting interpretable fuzzy rules from RBF networks, Neural Processing Letters, 17 (2003), 149--164
-
[6]
T. Kondo, A. S. Pandya, GMDH-type Neural Networks with a Feedback Loop and their Application to the Identification of Large-spatial Air Pollution Patterns, Proceedings of the 39th SICE annual conference. International session papersq, 2000 (2000), 19--24