Application of T-S Fuzzy Neural Network in the Assessment of River Ecosystem Health

Article Preview

Abstract:

Healthy river ecosystem has been acknowledged as the object of river management, which is crucial for the sustainable development of cities. Simple and practical evaluation methods with great precision are necessary for the evaluation of river ecosystem health. Fuzzy system has been widely used in evaluation and decision making for its simple reasoning and the adoption of experts knowledge. However, much artificial intervention decreases the precision. Neural network has a strong ability of self-leaning while it is not good at expressing rule-based knowledge. The T-S fuzzy neural network model combines the advantages of fuzzy system and neural network. In this paper, the T-S fuzzy neural network model was used to establish a river ecosystem health evaluation model. Results show that the combination of T-S fuzzy model and neural network eliminates the influences of subjective factors and improve the final precisions efficiently.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 726-731)

Pages:

958-962

Citation:

Online since:

August 2013

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] X. Li, C. Su, and S. Wang. Computer Engineering, no.6, vol.35(2009), pp.200-201.

Google Scholar

[2] S. Chen, "Engineering fuzzy set theory and application," Beijing: National Defense Industry Press, 1998, pp.41-58 (in Chinese).

Google Scholar

[3] X. Liu, Z. Hao. Proceedings of the 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery(FSKD on vol.2, pp.917-920, 26-28 July 2011.

Google Scholar

[4] Takagi T, Sugeno M. IEEE Trans. Syst. ,Man,Cyber.1985,SMC-15:116~132.

Google Scholar

[5] Plamen P. Angelov, and Dimitar P. Filev.. IEEE Trans. Syst. ,Man,Cyber vol. 34, no. 1, Feb 2004,pp.484-498.

Google Scholar

[6] S. Pang,Y.Zhou,X. Ding. Proceedings of the 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery(FSKD), pp.414-418, 26-28 July 2011.

Google Scholar

[7] Y.Zhang. Proceedings of the 8th World Congress on Intelligent Control and Automation July 6-9 2010,pp.466-470.

Google Scholar

[8] F. Gao, S. Lei, and H. Pang. Transactions of the CSAE, vol. 19, no. 4, pp.85-87, 2003.

Google Scholar

[9] G. Wang, and K. Zhu. Chinese Journal of Underground Space and Engineering, vol.5, pp.201-206, 2009 (in Chinese).

Google Scholar

[10] D. Zhao, H. Liu, and J. Zhang. Computer Engineering and Applications, vol. 45, no. 17, pp.85-87, 2009.

Google Scholar

[11] X. Huang, X. Shi, S. Liu, and L. Jin. Plateau Meteorology, vol.28, no.6, pp.1408-1413, 2009.

Google Scholar