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Top Document: comp.ai.neural-nets FAQ, Part 7 of 7: Hardware Previous Document: Neural Network hardware? Next Document: What to do with missing/incomplete data? See reader questions & answers on this topic! - Help others by sharing your knowledge There are vast numbers of published neural network applications. If you don't find something from your field of interest below, try a web search. Here are some useful search engines: http://www.google.com/ http://search.yahoo.com/ http://www.altavista.com/ http://www.deja.com/ General ------- o The Pacific Northwest National Laboratory: http://www.emsl.pnl.gov:2080/proj/neuron/neural/ including a list of commercial applications at http://www.emsl.pnl.gov:2080/proj/neuron/neural/products/ o The Stimulation Initiative for European Neural Applications: http://www.mbfys.kun.nl/snn/siena/cases/ o The DTI NeuroComputing Web's Applications Portfolio: http://www.globalweb.co.uk/nctt/portfolo/ o The Applications Corner, NeuroDimension, Inc.: http://www.nd.com/appcornr/purpose.htm o The BioComp Systems, Inc. Solutions page: http://www.bio-comp.com o Chen, C.H., ed. (1996) Fuzzy Logic and Neural Network Handbook, NY: McGraw-Hill, ISBN 0-07-011189-8. o The series Advances in Neural Information Processing Systems containing proceedings of the conference of the same name, published yearly by Morgan Kauffman starting in 1989 and by The MIT Press in 1995. Agriculture ----------- o P.H. Heinemann, Automated Grading of Produce: http://server.age.psu.edu/dept/fac/Heinemann/phhdocs/visionres.html o Deck, S., C.T. Morrow, P.H. Heinemann, and H.J. Sommer, III. 1995. Comparison of a neural network and traditional classifier for machine vision inspection. Applied Engineering in Agriculture. 11(2):319-326. o Tao, Y., P.H. Heinemann, Z. Varghese, C.T. Morrow, and H.J. Sommer III. 1995. Machine vision for color inspection of potatoes and apples. Transactions of the American Society of Agricultural Engineers. 38(5):1555-1561. Automotive ---------- o "No Hands Across America Journal" - steering a car: http://cart.frc.ri.cmu.edu/users/hpm/project.archive/reference.file/Journal.html Photos: http://www.techfak.uni-bielefeld.de/ags/ti/personen/zhang/seminar/intelligente-autos/tour.html Chemistry --------- o PNNL, General Applications of Neural Networks in Chemistry and Chemical Engineering: http://www.emsl.pnl.gov:2080/proj/neuron/neural/bib/chemistry.html. o Prof. Dr. Johann Gasteiger, Neural Networks and Genetic Algorithms in Chemistry: http://www2.ccc.uni-erlangen.de/publications/publ_topics/publ_topics-12.html o Roy Goodacre, pyrolysis mass spectrometry: http://gepasi.dbs.aber.ac.uk/roy/pymshome.htm and Fourier transform infrared (FT-IR) spectroscopy: http://gepasi.dbs.aber.ac.uk/roy/ftir/ftirhome.htm contain applications of a variety of NNs as well as PLS (partial least squares) and other statistical methods. o Situs, a program package for the docking of protein crystal structures to single-molecule, low-resolution maps from electron microscopy or small angle X-ray scattering: http://chemcca10.ucsd.edu/~situs/ o An on-line application of a Kohonen network with a 2-dimensional output layer for prediction of protein secondary structure percentages from UV circular dichroism spectra: http://www.embl-heidelberg.de/~andrade/k2d/. Criminology ----------- o Computer Aided Tracking and Characterization of Homicides and Sexual Assaults (CATCH): http://lancair.emsl.pnl.gov:2080/proj/neuron/papers/kangas.spie99.abs.html Face recognition ---------------- o Face Recognition Home Page: http://www.cs.rug.nl/~peterkr/FACE/face.html o Konen, W., "Neural information processing in real-world face-recognition applications," http://www.computer.muni.cz/pubs/expert/1996/trends/x4004/konen.htm o Jiang, Q., "Principal Component Analysis and Neural Network Based Face Recognition," http://people.cs.uchicago.edu/~qingj/ThesisHtml/ o Lawrence, S., Giles, C.L., Tsoi, A.C., Back, A.D. (1997), "Face Recognition: A Convolutional Neural Network Approach," IEEE Transactions on Neural Networks, 8, 98-113, http://www.neci.nec.com/~lawrence/papers/face-tnn97/latex.html Finance and economics --------------------- o Athanasios Episcopos, References on Neural Net Applications to Finance and Economics: http://www.compulink.gr/users/episcopo/neurofin.html o Franco Busetti, Heuristics and artificial intelligence in finance and investment: http://www.geocities.com/francorbusetti/ o Trippi, R.R. & Turban, E. (1993), Neural Networks in Finance and Investing, Chicago: Probus. o Zirilli, J.S. (1996), Financial Prediction Using Neural Networks, International Thomson Publishing, ISBN 1850322341, http://www6.bcity.com/mjfutures/ o Andreas S. Weigend, Yaser Abu-Mostafa, A. Paul N. Refenes (eds.) (1997) Decision Technologies for Financial Engineering: Proceedings of the Fourth International Conference on Neural Networks in the Capital Markets (Nncm '96) Publisher: World Scientific Publishing Company, ISBN: 9810231245 Games, sports, gambling ----------------------- o General: Jay Scott, Machine Learning in Games: http://satirist.org/learn-game/index.html METAGAME Game-Playing Workbench: ftp://ftp.cl.cam.ac.uk/users/bdp/ R.S. Sutton, "Learning to predict by the methods of temporal differences", Machine Learning 3, p. 9-44 (1988). David E. Moriarty and Risto Miikkulainen (1994). "Evolving Neural Networks to Focus Minimax Search," In Proceedings of Twelfth National Conference on Artificial Intelligence (AAAI-94, Seattle, WA), 1371-1377. Cambridge, MA: MIT Press, http://www.cs.utexas.edu/users/nn/pages/publications/neuro-evolution.html Games World '99 at http://gamesworld99.free.fr/menuframe.htm o Backgammon: G. Tesauro and T.J. Sejnowski (1989), "A Parallel Network that learns to play Backgammon," Artificial Intelligence, vol 39, pp. 357-390. G. Tesauro and T.J. Sejnowski (1990), "Neurogammon: A Neural Network Backgammon Program," IJCNN Proceedings, vol 3, pp. 33-39, 1990. G. Tesauro (1995), "Temporal Difference Learning and TD-Gammon," Communications of the ACM, 38, 58-68, http://www.research.ibm.com/massive/tdl.html Pollack, J.P. and Blair, A.D. (1997), "Co-Evolution in the Successful Learning of Backgammon Strategy," Brandeis University Computer Science Technical Report CS-97-193, http://www.demo.cs.brandeis.edu/papers/long.html#hcgam97 o Bridge: METAGAME: ftp://ftp.cl.cam.ac.uk/users/bdp/ He Yo, Zhen Xianjun, Ye Yizheng, Li Zhongrong (19??), "Knowledge acquisition and reasoning based on neural networks - the research of a bridge bidding system," INNC '90, Paris, vol 1, pp. 416-423. M. Kohle and F. Schonbauer (19??), "Experience gained with a neural network that learns to play bridge," Proc. of the 5th Austrian Artificial Intelligence meeting, pp. 224-229. o Checkers/Draughts: Mark Lynch (1997), "NeuroDraughts: an application of temporal difference learning to draughts," http://www.ai.univie.ac.at/~juffi/lig/Papers/lynch-thesis.ps.gz Software available at http://satirist.org/learn-game/archive/NeuroDraughts-1.00.zip K. Chellapilla and D. B. Fogel, "Co-Evolving Checkers Playing Programs using Only Win, Lose, or Draw," SPIE's AeroSense'99: Applications and Science of Computational Intelligence II, Apr. 5-9, 1999, Orlando, Florida, USA, http://vision.ucsd.edu/~kchellap/Publications.html David Fogel (1999), Evolutionary Computation: Toward a New Philosophy of Machine Intelligence (2nd edition), IEEE, ISBN: 078035379X David Fogel (2001), Blondie24: Playing at the Edge of AI, Morgan Kaufmann Publishers, ISBN: 1558607838 According to the publisher, this is: ... the first book to bring together the most advanced work in the general use of evolutionary computation for creative results. It is well suited for the general computer science audience. Here's the story of a computer that taught itself to play checkers far better than its creators ever could. Blondie24 uses a program that emulates the basic principles of Darwin evolution to discover on its own how to excel at the game. Through this entertaining story, the book provides the reader some of the history of AI and explores its future. Unlike Deep Blue, the celebrated chess machine that beat Garry Kasparov, the former world champion chess player, this evolutionary program didn't have access to other games played by human grand masters, or databases of moves for the endgame. It created its own means for evaluating the patterns of pieces that it experienced by evolving artificial neural networks--mathematical models that loosely describe how a brain works. See http://www.natural-selection.com/NSIPublicationsOnline.htm for a variety of online papers by Fogel. Not NNs, but classic papers: A.L. Samuel (1959), "Some studies in machine learning using the game of checkers," IBM journal of Research and Development, vol 3, nr. 3, pp. 210-229. A.L. Samuel (1967), "Some studies in machine learning using the game of checkers 2 - recent progress," IBM journal of Research and Development, vol 11, nr. 6, pp. 601-616. o Chess: Sebastian Thrun, NeuroChess: http://satirist.org/learn-game/systems/neurochess.html Luke Pellen, Octavius: http://home.seol.net.au/luke/octavius/ Louis Savain (AKA Nemesis), Animal, a spiking neural network that the author hopes will learn to play a passable game of chess after he implements the motivation mechanism: http://home1.gte.net/res02khr/AI/Temporal_Intelligence.htm o Dog racing: H. Chen, P. Buntin Rinde, L. She, S. Sutjahjo, C. Sommer, D. Neely (1994), "Expert Prediction, Symbolic Learning, and Neural Networks: An Experiment on Greyhound Racing," IEEE Expert, December 1994, 21-27, http://ai.bpa.arizona.edu/papers/dog93/dog93.html o Football (Soccer): Kuonen Diego, "Statistical Models for Knock-out Soccer Tournaments", http://dmawww.epfl.ch/~kuonen/CALCIO/ (not neural nets, but relevant) o Go: David Stoutamire (19??), "Machine Learning, Game Play, and Go," Center for Automation and Intelligent Systems Research TR 91-128, Case Western Reserve University. http://www.stoutamire.com/david/publications.html David Stoutamire (1991), Machine Learning Applied to Go, M.S. thesis, Case Western Reserve University, ftp://ftp.cl.cam.ac.uk/users/bdp/ Schraudolph, N., Dayan, P., Sejnowski, T. (1994), "Temporal Difference Learning of Position Evaluation in the Game of Go," In: Neural Information Processing Systems 6, Morgan Kaufmann 1994, ftp://bsdserver.ucsf.edu/Go/comp/ P. Donnelly, P. Corr & D. Crookes (1994), "Evolving Go Playing Strategy in Neural Networks", AISB Workshop on Evolutionary Computing, Leeds, England, ftp://www.joy.ne.jp/welcome/igs/Go/computer/egpsnn.ps.Z or ftp://ftp.cs.cuhk.hk/pub/neuro/GO/techreports/egpsnn.ps.Z Markus Enzenberger (1996), "The Integration of A Priori Knowledge into a Go Playing Neural Network," http://www.cgl.ucsf.edu/go/Programs/neurogo-html/neurogo.html Norman Richards, David Moriarty, and Risto Miikkulainen (1998), "Evolving Neural Networks to Play Go," Applied Intelligence, 8, 85-96, http://www.cs.utexas.edu/users/nn/pages/publications/neuro-evolution.html Dahl, F. A. (1999), "Honte, a Go-playing program using neural nets", http://www.ai.univie.ac.at/icml-99-ws-games/papers/dahl.ps.gz o Go-Moku: Freisleben, B., "Teaching a Neural Network to Play GO-MOKU," in I. Aleksander and J. Taylor, eds, Artificial Neural Networks 2, Proc. of ICANN-92, Brighton UK, vol. 2, pp. 1659-1662, Elsevier Science Publishers, 1992 Katz, W.T. and Pham, S.P. "Experience-Based Learning Experiments using Go-moku", Proc. of the 1991 IEEE International Conference on Systems, Man, and Cybernetics, 2: 1405-1410, October 1991. o Olympics: E.M.Condon, B.L.Golden, E.A.Wasil (1999), "Predicting the success of nations at the Summer Olympics using neural networks", Computers & Operations Research, 26, 1243-1265. o Pong: http:// www.engin.umd.umich.edu/~watta/MM/pong/pong5.html o Reversi/Othello: David E. Moriarty and Risto Miikkulainen (1995). Discovering Complex Othello Strategies through Evolutionary Neural Networks. Connection Science, 7, 195-209, http://www.cs.utexas.edu/users/nn/pages/publications/neuro-evolution.html Yoshioka, T., Ishii, S., and Ito, M., Strategy acquisition for the game ``Othello'' based on reinforcement learning, IEICE Transactions on Information and Systems E82-D 12, 1618-1626, 1999, http://mimi.aist-nara.ac.jp/~taku-y/ o Tic-Tac-Toe/Noughts and Crosses: Fogel, David Bb (1993), "Using evolutionary programming to construct neural networks that are capable of playing tic-tac-toe," Intern. Conf. on Neural Networks 1993, IEEE, San Francisco, CA, pp. 875-880. Richard S. Sutton and Andrew G. Barto (1998), Reinforcement Learning: An Introduction The MIT Press, ISBN: 0262193981, http://www-anw.cs.umass.edu/~rich/book/the-book.html Yongzheng Zhang, Chen Teng, Sitan Wei (2000), "Game playing with Evolutionary Strategies and Modular Neural Networks: Tic-Tac-Toe," http://www.cs.dal.ca/~mheywood/GAPproject/EvolvingGamePlay.html Rob Ellison, "Neural Os and Xs," http://www.catfood.demon.co.uk/beta/game.html (An online Javascript demo, but you may not live long enough to teach the network to play a mediocre game. I'm not sure what kind of network it uses, but maybe you can figure that out if you read the source.) http://listserv.ac.il/~dvorkind/TicTacToe/main_doc.htm, Java classes by Tsvi Dvorkind, using reinforcement learning. Industry -------- o PNNL, Neural Network Applications in Manufacturing: http://www.emsl.pnl.gov:2080/proj/neuron/neural/bib/manufacturing.html. o PNNL, Applications in the Electric Power Industry: http://www.emsl.pnl.gov:2080/proj/neuron/neural/bib/power.html. o PNNL, Process Control: http://www.emsl.pnl.gov:2080/proj/neuron/neural/bib/process.html. o Raoul Tawel, Ken Marko, and Lee Feldkamp (1998), "Custom VLSI ASIC for Automotive Applications with Recurrent Networks", http://www.jpl.nasa.gov/releases/98/ijcnn98.pdf o Otsuka, Y. et al. "Neural Networks and Pattern Recognition of Blast Furnace Operation Data" Kobelco Technology Review, Oct. 1992, 12 o Otsuka, Y. et al. "Applications of Neural Network to Iron and Steel Making Processes" 2. International Conference on Fuzzy Logic and Neural Networks, Iizuka, 1992 o Staib, W.E. "Neural Network Control System for Electric Arc Furnaces" M.P.T. International, 2/1995, 58-61 o Portmann, N. et al. "Application of Neural Networks in Rolling Automation" Iron and Steel Engineer, Feb. 1995, 33-36 o Gorni, A.A. (2000), "The modelling of hot rolling processes using neural networks: A bibliographical review", http://www.geocities.com/SiliconValley/5978/neural_1998.html o Murat, M. E., and Rudman, A. J., 1992, Automated first arrival picking: A neural network approach: Geophysical Prospecting, 40, 587-604. Materials science ----------------- o Phase Transformations Research Group (search for "neural"): http://www.msm.cam.ac.uk/phase-trans/pubs/ptpuball.html Medicine -------- o PNNL, Applications in Medicine and Health: http://www.emsl.pnl.gov:2080/proj/neuron/neural/bib/medicine.html. Music ----- o Mozer, M. C. (1994), "Neural network music composition by prediction: Exploring the benefits of psychophysical constraints and multiscale processing," Connection Science, 6, 247-280, http://www.cs.colorado.edu/~mozer/papers/music.html. o Griffith, N., and Todd, P.M., eds. (1999), Musical Networks: Parallel Distributed Perception and Performance, Cambridge, MA: The MIT Press, ISBN 0-262-07181-9. Robotics -------- o Institute of Robotics and System Dynamics: http://www.robotic.dlr.de/LEARNING/ o UC Berkeley Robotics and Intelligent Machines Lab: http://robotics.eecs.berkeley.edu/ o Perth Robotics and Automation Laboratory: http://telerobot.mech.uwa.edu.au/ o University of New Hampshire Robot Lab: http://www.ece.unh.edu/robots/rbt_home.htm Weather forecasting and atmospheric science ------------------------------------------- o UBC Climate Prediction Group: http://www.ocgy.ubc.ca/projects/clim.pred/index.html o Artificial Intelligence Research In Environmental Science: http://www.salinas.net/~jpeak/airies/airies.html o MET-AI, an mailing list for meteorologists and AI researchers: http://www.comp.vuw.ac.nz/Research/met-ai o Caren Marzban, Ph.D., Research Scientist, National Severe Storms Laboratory: http://www.nhn.ou.edu/~marzban/ o David Myers's references on NNs in atmospheric science: http://terra.msrc.sunysb.edu/~dmyers/ai_refs Weird ----- Zaknich, Anthony and Baker, Sue K. (1998), "A real-time system for the characterisation of sheep feeding phases from acoustic signals of jaw sounds," Australian Journal of Intelligent Information Processing Systems (AJIIPS), Vol. 5, No. 2, Winter 1998. Abstract This paper describes a four-channel real-time system for the detection and measurement of sheep rumination and mastication time periods by the analysis of jaw sounds transmitted through the skull. The system is implemented using an 80486 personal computer, a proprietary data acquisition card (PC-126) and a custom made variable gain preamplifier and bandpass filter module. Chewing sounds are transduced and transmitted to the system using radio microphones attached to the top of the sheep heads. The system's main functions are to detect and estimate rumination and mastication time periods, to estimate the number of chews during the rumination and mastication periods, and to provide estimates of the number of boli in the rumination sequences and the number of chews per bolus. The individual chews are identified using a special energy threshold detector. The rumination and mastication time periods are determined by neural network classifier using a combination of time and frequency domain features extracted from successive 10 second acoustic signal blocks. User Contributions:Comment about this article, ask questions, or add new information about this topic:Top Document: comp.ai.neural-nets FAQ, Part 7 of 7: Hardware Previous Document: Neural Network hardware? Next Document: What to do with missing/incomplete data? 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PDP++ is a neural-network simulation system written in C++, developed as an advanced version of the original PDP software from McClelland and Rumelhart's "Explorations in Parallel Distributed Processing Handbook" (1987). The software is designed for both novice users and researchers, providing flexibility and power in cognitive neuroscience studies. Featured in Randall C. O'Reilly and Yuko Munakata's "Computational Explorations in Cognitive Neuroscience" (2000), PDP++ supports a wide range of algorithms. These include feedforward and recurrent error backpropagation, with continuous and real-time models such as Almeida-Pineda. It also incorporates constraint satisfaction algorithms like Boltzmann Machines, Hopfield networks, and mean-field networks, as well as self-organizing learning algorithms, including Self-organizing Maps (SOM) and Hebbian learning. Additionally, it supports mixtures-of-experts models and the Leabra algorithm, which combines error-driven and Hebbian learning with k-Winners-Take-All inhibitory competition. PDP++ is a comprehensive tool for exploring neural network models in cognitive neuroscience.