Intelligent control for modeling of real-time reservoir operation, part II: Artificial neural network with operating rule curves

Hydrological Processes

Volumn 19, Issue 7, 2005, Pages 1431-1444

  Chang, Ya Ting ^a   Chang, Li Chiu ^a   Chang, Fi John ^a  

^a NATIONAL TAIWAN UNIVERSITY   (Taiwan)

Author keywords

Adaptive network based fuzzy inference system; Artificial neural network; Fuzzy rule base; Genetic algorithm; Reservoir operation

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; CONTROL SYSTEMS; FUZZY SETS; GENETIC ALGORITHMS; INFERENCE ENGINES; INTELLIGENT CONTROL; KNOWLEDGE ACQUISITION; NEURAL NETWORKS;

ADAPTIVE NETWORK-BASED FUZZY INFERENCE SYSTEMS (ANFIS); FUZZY RULE BASE (FRB); INTELLIGENT CONTROL SYSTEMS; KNOWLEDGE EXTRACTION;

RESERVOIRS (WATER);

CONTROL SYSTEM; GENETIC ALGORITHM; NEURAL NETWORK; RESERVOIR;

ARTIFICIAL NEURAL NETWORK; CONTROL SYSTEM; GENETIC ALGORITHM; REAL TIME; RESERVOIR;

EID: 17844370548     PISSN: 08856087     EISSN: None     Source Type: Journal    
DOI: 10.1002/hyp.5582     Document Type: Article

References (29)

1. Altug S, Chow MY, Trussell HJ. 1999. Fuzzy inference systems implemented on neural architectures for motor fault detection and diagnosis. IEEE Transactions on Industrial Electronics 46(6): 1069-1079.
2. Becerikli Y, Konar AF, Samad T. 2003. Intelligent optimal control with dynamic neural networks. Neural Networks 16(2): 251-259.
3. Cai XM, Mckinney DC, Lasdon LS. 2001. Solving nonlinear water management models using a combined genetic algorithm and linear programming approach. Advances in Water Resources 24(6): 667-676.
4. Chandramouli V, Kuppusamy KA, Manikandan K. 2002. Study on water sharing in a multi-reservoir system using a dynamic programming-neural network model. International Journal of Water Resources Development 18 3): 425-438.
5. Chang FJ, Chen L. 1998. Real-coded genetic algorithin for rule-based flood control reservoir management. Water Resource Management, EWRA 12(3): 185-198.
6. Chang FJ, Hui SC, Chen YC. 2002. Reservoir operation using grey fuzzy stochastic dynamic programming. Hydrological Processes 16: 2395-2408.
7. Chang LC, Chang FJ. 2001. Intelligent control of modeling of real time reservoir operation. Hydrological Processes 15: 1621-1634.
8. Davies RM, Watton J. 1995. Intelligent control of an electrohydraulic motor drive system. Mechatronics 5(5): 527-540.
9. Djukanovic MB, Calovic MS, Vesovic BV, Sobajic DJ. 1997. Neuro-fuzzy controller of low head hydropower plants using adaptive-network based fuzzy inference. IEEE Transactions on Energy Conversion 12 4): 375-381.
10. Dou C, Woldt W, Bogardi I. 1999. Fuzzy rule-based approach to describe solute transport in the unsaturated zone. Journal of Hydrology 220(1-2): 74-85.
11. Dubrovin T, Jolma A, Turunen E. 2002. Fuzzy model for real-time reservoir operation. Journal of Water Resources Planning and Management 128(1): 66-73.
12. Hasebe M, Nagayama Y. 2002. Reservoir operation using the neural network and fuzzy systems for dam control and operation support. Advances in Engineering Software 33(5): 245-260.
13. Holland JH. 1975. Adaptation in Natural and Artificial Systems, 2nd edn. Massachusetts Institute of Technology: Cambridge, MA.
14. Hsu SK. 1995. Shortage indices for water-resources planning in Taiwan. Journal of Water Resources Planning and Management 121(2): 119-131.
15. Hydrologic Engineering Center. 1996. Reservoir yield, generalized computer program 23-J2-L245. US Army, Corps of Engineers, Davis, CA.
16. Jain SK, Das A, Srivastava DK. 1999. Application of ANN for reservoir inflow prediction and operation. Journal of Water Resources Planning and Management 125(5): 263-271.
17. Jang J-SR. 1993. ANFIS: adaptive-network-based fuzzy inference system. IEElE Transactions on Systems, Man, and Cybernetics 23: 665-685.
18. Kelman J, Stedinger JR, Copper LA, Hsu E, Yuan SQ. 1990. Sampling stochastic dynamic programming applied to reservoir operation. Water Resources Research 26(3): 447-454.
19. Lin CL, Su HW. 2000. Intelligent control theory in guidance and control system design: an overview. Proceedings of the National Science Council, ROC 24(1): 15-30.
20. Mantawy AH, Abdel-Magid YL, Selim SZ. 1999. A new genetic-based Tabu search algorithm for unit commitment problem. Electric Power Systems Research 49(2): 71-78.
21. Perera BJC, Codner G. 1998. Computational improvement for stochastic dynamic programming models of urban water supply reservoirs. Journal of the American Water Resources Association 34(2): 267-278.
22. Raman H, Chandramouli V. 1996. Deriving a general operating policy for reservoirs using neural network. Journal of Water Resources Planning and Management 122(5): 342-347.
23. Russell SO, Campbell PF. 1996. Reservoir operating rules with fuzzy programming. Journal of Water Resources Planning and Management 122(3): 165-170.
24. Shrestha BP, Duckstein L, Stakhiv EZ. 1996. Fuzzy rule-based modeling of reservoir operation. Journal of Water Resources Planning and Management 122(4): 262-269.
25. Takagi T, Sugeno M. 1983. Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics 15: 116-132.
26. Thomas LC, Archibald TW, McKinnon KIM. 1997. An aggregate stochastic dynamic programming model of multiple reservoir systems. Water Resources Research 33: 333-340.
27. Wang QJ. 1991. The genetic algorithm and its application to calibrating conceptual rainfall-runoff models. Water Resources Research 27 2467-2471.
28. Yeh W. 1985. Reservoir management and optimization models: a state of the art review. Water Resources Research 21: 1797-1818.
29. Zadeh LA. 1965. Fuzzy sets. Information and Control 8: 338-353.