Abstract
The current interest in artificial neural networks can be attributed, in part, to the development of the modern computer. Since the advent of inexpensive, efficient, highstorage capacity computers, there has been an information explosion in many scientific disciplines as researchers are able to acquire larger and more comprehensive data sets. The interpretation of much of these data often requires manual inspection by scientists, especially when traditional methods of analysis do not appear to find the important relationships in the data. Manual inspection of data can be repetitive, time consuming, and difficult when many variables are involved simultaneously. Several novel processing schemes have been devised that attempt to supplement traditional signal processing techniques in difficult applications. One such approach for finding interesting relationships in multivariate data is the field of artificial neural networks (ANN).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Adachi, M., Aihara, K., Kotani, M. 1991 “Pattern Dynamics of Chaotic Neural Networks with Nearest-Neighbor Couplings”, IEEE International Symposium on Circuits and Systems, 2, p. 1180–1183.
Aihara, K. 1991 “Chaotic Dynamics in Nerve Membranes and its Modelling with an Artificial Neuron”, IEEE International Symposium on Circuits and Sytems, 3 p. 1457–1460.
Amari, S.-I. 1977 “Neural Theory of Association and Concept-Formation”. Biological Cybernetics, 26 p. 175–185.
Anderson, J.A. 1972 “A Simple Neural Network Generating an Interactive Memory”, Mathematical Biosciences, 14 p. 197–220.
Anderson, J.A., Silverstein, J.W., Ritz, S.A., Jones, R.S. 1977 “Distinctive Features, Categorical Perception, and Probability Learning: Some Applications of a Neural Model”, Psychological Review, 84 p. 413–451.
Anderson, J.A., Rosenfeld, E. 1988 “Neurocomputing: Foundations of Research”, MIT Press, Cambridge, MA.
Baum, E.B., Haussler, D. 1989 “What Size Net Gives Valid Generalization?”, Neural Computation, 1 p. 151–160. Block, H.D. 1962 “The Perceptron: A Model for Brain Functioning”, Reviews of Modern Physics, 34, p. 123-135.
Carpenter, G.A., Grossberg, S. 1987 “A Massively Parallel Architecture for a Self-Organizing Neural Pattern Recognition Machine”, Computer, Vision, Graphics, and Image Processing, 37 p. 54–115.
Carroll, S.M., Dickinson, B.W. 1989 “Construction of Neural Nets Using the Radon Transform”, IEEE International Joint Conference on Neural Networks, Washington, DC, 1 p. 607–611.
Chay, T.R. 1991 “Complex Oscillations and Chaos in a Simple Neuron Model”, IEEE International Joint Conference on Neural Networks, Seattle, WA, 2, p. 657–662.
Cooper, L.N. 1973 “A Possible Organization of Animal Memory and Learning”, In Proceedings of the Nobel Symposium on Collective Properties of Physical Systems, B. Lundquist and S. Lundquist(Eds.), Academic Press, New York, p. 252–264.
Cybenko, G. 1988 “Continuous Valued Neural Networks with Two Hidden Layers are Sufficient”, Technical Report, Dept. of Computer Sciences, Tufts University, Medford, MA.
Cybenko, G. 1989 “Approximation by Superpositions of a Sigmoidal Function”, Mathematics of Control, Signals, and Systems, 2(4), p. 303–314.
DARPA 1988 “DARPA Neural Network Study”, AFCEA International Press, Fairfax, VA.
Dayhoff, J.D. 1990 “Neural Network Architectures: An Introduction”, Van Nostrand Reinhold, New York.
De Schutter, E., Bower, J.M. 1993 “Sensitivity of Synaptic Plasticity to the Permeability of NMDA Channels: A Model of Long-Term Potentiation in Hippocampal Neurons”, Neural Computation, 5, p. 681-694.
Freeman, W.J. 1988 “Strange Attractors that Govern Mammalian Brain Dynamics Shown by Trajectories of Electroencephalographic (EEG) Potential”, IEEE Transactions on Circuits and Syterns, 35:(7), p. 781–783.
Funahashi, K.-I. 1989 “On the Approximate Realization of Continuous Mappings by Neural Networks”, Neural Networks, 2, p. 183–192.
Gibson, G.J., Cowan, C.F.N. 1990 “On the Decision Regions of Multilayer Perceptrons”, Proceedings of the IEEE, 78:(10) p. 1590–1594.
Giles, C.L., Sun, G.Z., Chen, H.H., Lee, Y.C., Chen, D. 1990 “Higher Order Recurrent Networks and Grammatical Inference”, In Advances in Neural Information Processing Systems, 2, D. S. Touretzky (Ed.), Morgan Kaufmann, San Mateo, CA.
Giles, C.L., Miller, C.B., Chen, D., Sun, G.Z., Chen, H.H., Lee, Y.C. 1992 “Extracting and Learning an Unknown Grammar with Recurrent Neural Networks”, In Advances in Neural Information Processing Systems, 4, J.E. Moody, S.J. Hanson, and R.P. Lippmann (Eds.), Morgan Kaufman, San Mateo, CA.
Girosi, F., Poggio, T. 1989 “Representation Properties of Networks: Kolmogorov’s theorem is irrelevant”, Neural Computation, 1:(4) p. 465–469.
Gray, R.M. 1984 “Vector Quantization”, IEEE ASSP Magazine, 1 p. 4–29.
Grossberg, S. 1973 “Contour Enhancement, Short Term Memory, and Constancies in Reverberating Neural Networks”, Studies in Applied Mathematics, 52 p. 213–257.
Grossberg, S. 1976 “Adaptive Pattern Classification and Universal Recoding: I. Parallel Development and Coding of Neural Feature Detectors”, Biological Cybernetics, 23, p. 121–134.
Hansen, L.K., Liisberg, C., Salamon, P. 1992 “Ensemble Methods for Handwritten Digit Recognition”, In Neural Networks for Signal Processing II-Proceedings of the 1992 IEEE Workshop, Copenhagen, Denmark, p. 333-342.
Hebb, D.O. 1949 “The Organization of Behavior”, Wiley, New York. Hecht-Nielsen, R. 1990 “Neurocomputing”, Addison-Wesley, Reading, MA.
Hopfield, J.J. 1982 “Neural Networks and Physical Systems with Emergent Collective Computational Abilities”, Proceedings of the National Academy of Sciences, 79, p. 2554–2558.
Hornik, K., Stinchcombe, M., White, H. 1989 “Multilayer Feedforward Networks are Universal Approximators”, Neural Networks, 2, p. 359–366.
Hush, D.R., Home, B.G. 1993 “Progress in Supervised Neural Networks: What’s New Since Lippmann”, IEEE Signal Processing Magazine, 10:(l) p. 8–39.
Intrator, N., Cooper, L.N. 1992 “Objective Function Formulation of the BCM Theory of Visual Cortical Plasticity: Statistical Connections, Stability Conditions”, Neural Networks, 5, p. 3–17.
Jacobs, R.A., Jordan, M.I. 1991 “Adaptive Mixtures of Local Experts”, Neural Computation, 3, p. 79–87.
Jones, L.K. 1990, “Constructive Approximations for Neural Networks by Sigmoidal Functions”, Proceedings of the IEEE, 78:(10) p. 1586–1589.
Jones, L.K. 1992 “A Simple Lemma on Greedy Approximation in Hilbert Space and Convergence Rates for Projection Pursuit Regression and Neural Network Training”, The Annals of Statistics, 20:(1) p. 608–613.
Knight, K. 1990 “Connectionist Ideas and Algorithms”, Communications of the ACM, 33:(11). p. 59–74.
Kolmogorov, A.N. 1957 “On the Representation of Continuous Functions of Several Variables by Superposition of Continuous Functions of One Variable and Addition”, Dokl. Akad. Nauk SSSR, 114, p. 953–956.
Kohonen, T. 1989 “Self-Organization and Associative Memory”, Springer-Verlag, Berlin, Germany.
Kohonen, T. 1990 “The Self-Organizing Map”, Proceedings of the IEEE, 78:(9), p. 1464–1480.
Kosko, B. (Ed.) 1992 “Neural Networks for Signal Processing”, Prentice Hall, Englewood Cliffs, NJ. Lau, C. (Ed.) 1992 “Neural Networks: Theoretical Foundations and Analysis. IEEE Press, New York.
Linde, Y., Buzo, A., Gray, R.M. 1980 “An Algorithm for Vector Quantization”, IEEE Transactions on Communications, COM-28, p. 84–95.
Lippmann, R.P. 1987 “An Introduction to Computing with Neural Nets”, IEEE ASSP Magazine, 4, p. 4–22.
Lorentz, G.G. 1976 “The 13th Problem of Hilbert”, In Mathematical Developments Arising from Hilbert Problems”, F. E. Browder(Ed.), American Mathematical Society, Providence, RI, p. 419–430.
Ljung, L., Sjoberg, J. 1992 “A System Identification Perspective on Neural Nets”, Neural Networks for Signal Processing II-Proceedings of the 1992 IEEE Workshop, Helsingor, Denmark, p. 423-435.
McClelland, J.L., Rumelhart, D.E., and the ia]PDP Research Group 1986 “Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Volume 2: Psychological and Biological Models, MIT Press, Cambridge, MA.
Moore, B., Poggio, T. 1988 “Representations Properties of Multilayer Feedforward Networks”, In Abstracts of the First Annual INNS Meeting, Pergamon Press, New York, p. 502.
Parios, A., Atiya, A., Chong, K., Tsai, W., Fernandez, B. 1991 “Recurrent Multilayer Perceptron for Nonlinear System Identification”, IEEE International Joint Conference on Neural Networks, Seattle, WA, 2, p. 537–540.
Perrone, M.P., Cooper, L.N. 1993 “Learning from What’s Been Learned: Supervised Learning in Multi-Neural Network Systems”, Proceedings of the World Congress on Neural Networks, Portland, OR, p. 354-357.
Perrone, M.P., Cooper, L.N. in press “When Networks Disagree: Ensemble Methods for Neural Networks”, In Neural Networks for Speech and Image Processing, R. J. Mammone (Ed.), Chapman Hall.
Pollack, J.B., 1991 “The Induction of Dynamical Recognizers”, Machine Learning, 7, p. 227–252. Rosenblatt, F. 1958 “The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain”, Psychological Review, 65, p. 386-408.
Rumelhart, D.E., Hinton, G.E., Williams, R.J. 1986a “Learning Internal Representations by Error Propagation”, In Parallel Distributed Processing: Explorations in the Microstructures of Cognition, Volume 1: Foundations, D.E. Rumelhart and J.L. McClelland(Eds.), MIT Press, Cambridge, MA, p. 318–362.
Rumelhart, D.E., Hinton, G.E., Williams, R.J. 1986b “Learning Representations by Back-Propagating Errors”, Nature, 323 p. 533–536.
Rumelhart, D.E., McClelland, J.L., and the ia]PDP Research Group 1986c “Parallel Distributed Processing: Explorations in the Microstructure of Cognition”, Volume 1: Foundations, MIT Press, Cambridge, MA.
Rumelhart, D.E., Zipser, D. 1985 “Feature Discovery by Competitive Learning”, Cognitive Science, 9, p. 75–112.
Solla, S.A. 1992 “Capacity Control in Classifiers for Pattern Recognition”, In Neural Networks for Signal Processing II-Proceedings of the 1992 IEEE Workshop, Helsingor, Denmark, p. 255-266.
Stinchcombe, M., White, H. 1989 “Universal Approximation Using Feedforward Networks with Non-Sigmoid Hidden Layer Activation Functions”, IEEE International Joint Conference on Neural Networks, Washington, DC, 1, p. 613–617.
Tumey, D.M., Morton, P.E., Ingle, D.F., Downey, C.W., Schnurer, J.H. 1991 “Neural Network Classification of EEG Using Chaotic Preprocessing and Phase Space Reconstruction”, Proceedings of the 1991 IEEE Seventeenth Annual Northeast Bioengineering Conference, p. 51-52.
Widrow, B., Hoff, M.E. 1960 “Adaptive Switching Circuits”, IRE WESCON Convention Record, 4, p. 96–104.
Williams, R.J., Zipser, D. 1988 “A Learning Algorithm for Continually Running Fully Recurrent Neural Networks”, ICS Report 8805, University of California, San Diego, CA.
Yao, Y., Freeman, W.J. 1989 “Pattern Recognition in Olfactory Systems: Modeling and Simulation”, IEEE International Joint Conference on Neural Networks, Washington, DC, 1, p. 699–704.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1994 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Clothiaux, E.E., Bachmann, C.M. (1994). Neural Networks and Their Applications. In: Hewitson, B.C., Crane, R.G. (eds) Neural Nets: Applications in Geography. The GeoJournal Library, vol 29. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-1122-5_2
Download citation
DOI: https://doi.org/10.1007/978-94-011-1122-5_2
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-010-4490-5
Online ISBN: 978-94-011-1122-5
eBook Packages: Springer Book Archive