Water demand prediction model based on radial basis function neural network

2009 1st International Conference on Information Science and Engineering, ICISE 2009

Volumn , Issue , 2009, Pages 5295-5298

  Chang, Mingqi ^a,b   Liu, Junping ^c  

^a CHANG'AN UNIVERSITY   (China)

^b China Irrigation and Drainage Development Center   (China)

^c ZHEJIANG UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Dynamic clustering algorithm; Neural network; Radial basis function; Water demand prediction

Indexed keywords

AGRICULTURAL WATER; ARTIFICIAL NEURAL NETWORK; CLUSTER CENTERS; CONVERGENCE SPEED; DISTRIBUTED STORAGE; DOMESTIC WATER; DYNAMIC CLUSTERING; DYNAMIC CLUSTERING ALGORITHM; FUZZY INFORMATION; HIDDEN LAYERS; HUMAN BRAIN; INDUSTRIAL WATER; INFLUENCING FACTOR; INITIAL WEIGHT VALUES; NETWORK LEARNING; NON-LINEARITY; NONCONVEXITY; NONLOCALITIES; OUTPUT LAYER; PARALLEL INFORMATION PROCESSING; RADIAL BASIS FUNCTION NEURAL NETWORKS; RADIAL BASIS FUNCTIONS; RBF NEURAL NETWORK; RELATIVE ERRORS; RURAL HOUSEHOLDS; SELF-LEARNING; URBAN-HOUSEHOLD; WATER CONSUMPTION; WATER DEMAND;

ATTITUDE CONTROL; CLUSTERING ALGORITHMS; DATA PROCESSING; FAULT TOLERANCE; FORECASTING; INFORMATION SCIENCE; LEARNING ALGORITHMS; MATHEMATICAL MODELS; QUALITY ASSURANCE; RADIAL BASIS FUNCTION NETWORKS; WATER SUPPLY;

NEURAL NETWORKS;

EID: 77952749576     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICISE.2009.1343     Document Type: Conference Paper

References (9)

1. JIAO Li cheng.Theory of neural network system.Xi'an:Xi Dian university press,1996.
2. Whitehead B A.Cooperatitive genetic evolution of radial basis function certers and widths for time series prediction.IEEE Transactions on Neural Networks,vol.7(4),pp.869-880,1996.
3. Lu Y W,Sundararajan N and Saratchandran P.Performance evaluation of sequential mininal radial basis function(RBF) neural network leaning algorithm.IEEE Transactions on Neural Networks,vol.9(6),pp.308-317,1998.
4. D.S. Broomhead and D. Lowe, Multi-variable functional interpolation and adaptive networks.Complex Systems,vol. 2(3),pp.321-355,1988.
5. J. Moody and C. Darken, Faster learning in networks of locally-tuned processing units.Neural Computation.vol. 1(2),pp. 281-294,1989.
6. A. Zhang and L. Zhang, RBF neural networks for the prediction of building interference effects.Computers & Structures. 2004(82): 2333-2339.
7. Salajegheh, E and Gholizadeh, S.Optimum design of structures by an improved genetic algorithm using neural networks[J].Advances in Engineering Software,vol..36 (11-12), pp.757-767,2005.
8. ZHU Mingxing and ZHANG Delong.Study on the Algorithms of Selecting the Radial Basis Function Center[J].Journal of ANHUI UNIVERSITY(NATURAL SCIENCES), vol. 24(1),pp.74-78,2000.
9. ZHOU Jun wu,SUN Chuan yao,WANG Fu li.Study and implement of radial basis function neural network[J].Mining and Metallurgy, vol.10(4),pp.71-75,2001.