Types of artificial neural networks

Results: 141



#Item
41Hydrol. Earth Syst. Sci., 16, 1151–1169, 2012 www.hydrol-earth-syst-sci.net[removed]doi:[removed]hess[removed] © Author(s[removed]CC Attribution 3.0 License.  Hydrology and

Hydrol. Earth Syst. Sci., 16, 1151–1169, 2012 www.hydrol-earth-syst-sci.net[removed]doi:[removed]hess[removed] © Author(s[removed]CC Attribution 3.0 License. Hydrology and

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Source URL: www.hydrol-earth-syst-sci.net

Language: English - Date: 2014-12-04 03:06:09
42IEEE PES 2001 Winter Meeting, January 28 - February 1, 2001, Columbus, Ohio USA PEAK LOAD FORECASTING USING ANALYZABLE STRUCTURED NEURAL NETWORK

IEEE PES 2001 Winter Meeting, January 28 - February 1, 2001, Columbus, Ohio USA PEAK LOAD FORECASTING USING ANALYZABLE STRUCTURED NEURAL NETWORK

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Source URL: homepage2.nifty.com

Language: English - Date: 2006-01-19 09:36:27
43Using an artificial neural network to classify black-capped chickadee (Poecile atricapillus) call note types Michael R. W. Dawson, Isabelle Charrier, and Christopher B. Sturdya兲 Department of Psychology, Centre for Neu

Using an artificial neural network to classify black-capped chickadee (Poecile atricapillus) call note types Michael R. W. Dawson, Isabelle Charrier, and Christopher B. Sturdya兲 Department of Psychology, Centre for Neu

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Source URL: www.bcp.psych.ualberta.ca

Language: English - Date: 2012-07-11 15:01:02
44User’s Guide to Pittnet Neural Network Educational Software  Brian Carnahan and Alice E. Smith Department of Industrial Engineering 1048 Benedum Hall University of Pittsburgh

User’s Guide to Pittnet Neural Network Educational Software Brian Carnahan and Alice E. Smith Department of Industrial Engineering 1048 Benedum Hall University of Pittsburgh

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Source URL: www.foretrade.com

Language: English - Date: 2009-09-11 07:24:22
45Microsoft Word - Eprints Cover Sheet.doc

Microsoft Word - Eprints Cover Sheet.doc

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Source URL: eprints.qut.edu.au

Language: English - Date: 2010-06-09 08:40:49
46Microsoft Word - V09N03P2.doc

Microsoft Word - V09N03P2.doc

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Source URL: www.bcp.psych.ualberta.ca

Language: English - Date: 2012-07-11 15:01:12
47An Empirical Study of Learning Speed in Back-Propagation Networks Scott E. Fahlman September 1988 CMU-CS[removed]

An Empirical Study of Learning Speed in Back-Propagation Networks Scott E. Fahlman September 1988 CMU-CS[removed]

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Source URL: www.foretrade.com

Language: English - Date: 2009-09-24 10:00:05
48in Proceedings of the 1999 International Joint Conference on Neural Network (pp. 304i–304vi)  304i Internal representation in networks of non-monotonic processing units David B. McCaughan

in Proceedings of the 1999 International Joint Conference on Neural Network (pp. 304i–304vi) 304i Internal representation in networks of non-monotonic processing units David B. McCaughan

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Source URL: www.bcp.psych.ualberta.ca

Language: English - Date: 2012-07-11 15:01:11
49Daniel Goldenholz BE707 Final Project Liquid Computing: A Real Effect ABSTRACT A novel approach to temporal pattern recognition was recently introduced in the literature, called liquid computing. The model used was very

Daniel Goldenholz BE707 Final Project Liquid Computing: A Real Effect ABSTRACT A novel approach to temporal pattern recognition was recently introduced in the literature, called liquid computing. The model used was very

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Source URL: www.lsm.tugraz.at

Language: English - Date: 2006-05-15 03:56:13
502 Reference This chapter contains a detailed description of all the functions in the Neural Network Based System Identification Toolbox. The information given here is more or less identical to that obtained from the onli

2 Reference This chapter contains a detailed description of all the functions in the Neural Network Based System Identification Toolbox. The information given here is more or less identical to that obtained from the onli

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Source URL: www.iau.dtu.dk

Language: English - Date: 1998-10-22 04:31:25