Application of Artificial Neuro-Fuzzy logic Inference System for predicting the microbiological pollution in fresh water

Journal of Applied Sciences

Volumn 8, Issue 2, 2008, Pages 309-315

  Bouharati, S ^a   Benmahammed, K ^a   Harzallah, D ^a   El Assaf, Y M ^b  

^a FERHAT ABBAS UNIVERSITY   (Algeria)

^b AMERICAN UNIVERSITY OF SHARJAH   (United Arab Emirates)

Author keywords

Artificial Neuro Fuzzy logic inference; E. coli detection; Fuzzy expert system; Hybrid intelligent system; Testing water

Indexed keywords

BACTERIA; COLIFORM BACTERIA; COMPUTER CIRCUITS; ESCHERICHIA COLI; EXPERT SYSTEMS; FUZZY INFERENCE; FUZZY LOGIC; FUZZY SYSTEMS; INTELLIGENT SYSTEMS; PARAMETER ESTIMATION; POLLUTION; POLLUTION DETECTION; POTABLE WATER; RIVER POLLUTION; SURFACE WATERS; WATER; WATER SUPPLY;

E. COLI DETECTION; ELECTRICAL CONDUCTIVITY; FUZZY EXPERT SYSTEMS; HYBRID INTELLIGENT SYSTEM; MICROBIOLOGICAL POLLUTIONS; NEURO-FUZZY INFERENCE SYSTEMS; NEURO-FUZZY LOGIC; PHYSICAL AND CHEMICAL PARAMETERS;

WATER POLLUTION;

EID: 41949117014     PISSN: 18125654     EISSN: 18125662     Source Type: Journal    
DOI: 10.3923/jas.2008.309.315     Document Type: Article

References (17)

1. Beth, A., B.A. Sproule, C.A. Naranjo and B. Türksen, 2002. Fuzzy pharmacology: Theory and application. Trends Pharmacol. Sci., 23 (9): 412-417.
2. Bezdek, C., 1981. Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York.
3. Brion, G.M., T.R. Neelakantan and S. Lingireddy, 2002. A neural-network-based classification scheme for sorting sources and ages of fecal contamination in water. Water Res., 36: 3765-3774.
4. Brown, M. and C. Harris, 1994a. Neurofuzzy Adaptive Modelling and Control. Hertfordshire, Prentice Hall International. UK.
5. Brown, M. and C.J. Harris, 1994b. Neurofuzzy Adaptive Modelling and Control. Hemel Hempstead, Prentice Hall. UK.
6. Chen, D.G., N.B. Haregreaves, D.M. Ware and Y. Liu, 2000. A Fuzzy logic model with genetic algorithm for analyzing fish stock-recruitment relationships. Can. J. Fish. Aquat. Sci., 57: 1878-1887.
7. Collins, C.H., M. Patricia and J.N. Lyne, 1995. Microbiological methods. Butterworth-Heinmann Ltd. Linacre House, Jordan Hill, Oxford OX2 8DP, pp: 121.
8. Fhayung, M. and G.Y. Gary, 2003. Combined numerical and linguistic knowledge representation and its application to medical diagnosis. IEEE Transactions on Systems, Man and Cybernetics Part A: Syst. Hum., 33 (2): 206-232.
9. Harris, C.J., X. Hong and Q. Gan, 2002. Adaptive Modelling, Estimation and Fusion from Data: A Neurofuzzy Approach. Berlin and Heidelberg, Germany: Springer-Verlag.
10. Jantzen, J., 1998. Neurofuzzy modelling. Technical University of Denmark, Lyngby, Denmark.
11. Kecman, V., 2001a. Learning and Soft Computing. Cambridge, MA: MIT Press/ Bradford Books.
12. Kecman, V., 2001b. Learning and Soft Computing: Support Vector Machines. Neura Networks and Fuzzy Logic Models. Boston, MA: MIT Press.
13. Li-Xin Wang, 1997. Modeling and control of hierarchical systems with fuzzy systems. Automatica J. IFAC., 33(6): 1041-1053.
14. Owens, R., D. S. Thompson and A.W. Timmerman, 1989. Indirect conductimetry; A novel approach, the conductometric enumeration of microbial pollution. Lett. Applied Microbiol., 9: 245-249.
15. Taha, T.A. and J. Ghosh, 1999. Symbolic interpretation of artificial neural networks. IEEE Trans. Knowl. Data Eng., 11: 448-463.
16. Tan, H., 1997. Cascade ARTMAP: Integrating neural computation and symbolic knowledge processing. IEEE Trans. Neural. Networks, 8: 237-250.
17. Wernier, S. and R. Sun, 2000. An Overview of Hybrid Neural Systems. In Hybrid Neural Systems, Wernier, S. and R. Sun (Eds.). Berlin, Germany-Springer-Verlag, pp: 1-13.