Data processing: artificial intelligence – Neural network – Learning task
Reexamination Certificate
2007-08-07
2007-08-07
Vincent, David (Department: 2129)
Data processing: artificial intelligence
Neural network
Learning task
C703S010000
Reexamination Certificate
active
10277595
ABSTRACT:
The present invention relates to a method and apparatus, based on the use of a neural network (NN), for (a) predicting important groundwater/surface water output/state variables, (b) optimizing groundwater/surface water control variables, and/or (c) sensitivity analysis, to identify physical relationships between input and output/state variables used to model the groundwater/surface water system or to analyze the performance parameters of the neural network.
REFERENCES:
patent: 5468088 (1995-11-01), Shoemaker et al.
patent: 5675504 (1997-10-01), Serodes et al.
“Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling”, L. L. Rogers, F. U. Dowla; Earth Sciences Dept, Lawrence Livermore Natl Lab. Water Resources Res., vol. 30, No. 2, pp. 457-481, Feb. 1994.
“A Neural Network based Parametrization Method for Distributed Parameter Identification”, M. Sun & C. Zheng, System Theory, 1998. Proceedings of the Thirtieth Southeastern Symposium on Mar. 8-10, 1998, pp. 361-365.
“Artificial Neural Networks in Hydrology. I: Preliminary Concepts”, by ASCE Task Committee, Rao S. Govindaraju, Journal of Hydrologic Engineering, vol. 5, No. 2, Apr. 2000, pp. 115-123.
“Artificial Neural Networks in Hydrology. II: Hydrologic Applications”, by ASCE Task Committee, Rao S. Govindaraju, Journal of Hydrologic Engineering, vol. 5, No. 2, Apr. 2000, pp. 124-137.
“Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling.” Leah L. Rogers and Farid U. Dowla; Earth Sciences Dept., Lawrence Livermore National Laboratory, Livermore, CA.Water Resources research.vol. 30, No. 2, pp. 457-481. Feb. 1994.
“Artificial Neural Network Modeling of Water Table Depth Fluctuations.” Paulin Coulibaly,1,2François Anctil,3Ramon Aravena,4and Bernard Bobee1.Water Resources research. vol. 37, No. 4, pp. 885-896, Apr. 2001.
“Optimization of Well Placement in a Gulf of Mexico Waterflooding Project.” Baris Guyaguler, SPE, Roland N. Home, SPE, Leah Rogers, SPE, and Jacob J. Rosenzweig. Copyright 2000.Society fo Petroleum Engineers Inc.SPE 63221, pp. 1-10. This paper was prepared for presentation at the 2000 SPE Annual Technical Conference and Exhibition held in Dallas, TX, Oct. 1-4, 2000.
“Computational intelligence tools for groundwater management.” M. Stundner, G. Zangl,Listen and Talk—Environmental Services, Baden, Austria. pp. 1-2.
“Use of Artificial neural networks and Fuzzy logic for integrated water management: Review of applications.” Project report.IHE Delft Hydroinformation. Delft 2000. pp. 1-10.
“The Application of Artificial Neural Networks for the Predication of Water Quality of Polluted Aquifer.” F. Gumrah, B. Oz, B. Guler and S. Evin.Water, Air and Soil Pollution. 119: pp. 275-294, 2000.
“Enhanced Water Management of Cascade Hydro System.” T. Stokelj, D. Paravan, and R. Golob. Proceedings of the Lasted International Conference Power and Energy Systems. Sep. 19-22, 2000, Marbella, Spain. pp. 431-436.
“An Evaluation of a Neural-Network Method for Aquifer Hydraulic Conductivities Estimation.” Charles S. Sawyer, Luke E.K. Achenie, and Karen K. Lieuallen.Intelligent Engineering Systems Through Artificial Neural Networks.vol. 4, Proceeding Intelligent Engineering Systems Through Artificial Neural Networks, St. Louis, MO, USA, pp. 13-16, Nov. 1994.
“Location Analysis in Ground-Water Remediation Using Neural Networks.” Virginia M. Johnson and Leah L. Rogers,Journal of Ground Water.vol. 33, No. 5, pp. 749-758, Sep./Oct. 1995.
Slide Presentation (Determining Optimal Pumping Policies for a Public Supply Wellfield Using a Computational Neural Network with Linear Programming Paper No. H32D-03.
Dissertation by Emery Coppola entitled: Optimal Pumping Policy for a Public Supply Wellfield Using Computational Neural Network with Decision-Making Methodology.
Coppola, Jr. Emery J.
Poulton Mary M.
Szidarovszky Ferenc
Oremland, P.C. Lawrence R.
Tran Mai T.
Vincent David
LandOfFree
Neural network based predication and optimization for... does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Neural network based predication and optimization for..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Neural network based predication and optimization for... will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-3863357