Skip to main content
SpringerLink
Log in

Data Analysis

  • Chapter
Neural Network Data Analysis Using Simulnet™

  • 157 Accesses

Abstract

This section will introduce a variety of approaches to the analysis of data. The primary focus will be on the application of neural network-based techniques to the tasks of prediction, classification, and function approximation. This section will therefore begin by discussing the following neural network functions that are available in Simulnet.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

References

  • Angus, John E. (1991). Criteria for choosing the best neural network: I. US Naval Health Research Center Report, Rpt No 91–16 25.

    Google Scholar 

  • Baker, G. L. and Gollub, J. P. (1990). Chaotic Dynamics, an introduction. Cambridge: Cambridge University Press.

    MATH  Google Scholar 

  • Baum, E. and Haussler, D. (1989). What size net give valid generalization? Neural Computation, 1(1): 151–160.

    Article  Google Scholar 

  • Bioch, J. C., Verbeke, W., and van Dijk, M. W. (1994). Neural networks: New tools for data analysis? In P. J. G. Lisboa and M. J. Taylor (Eds.), Proceedings of the Workshop on Neural Network Applications and Tools, Los Alamitos, CA: IEEE Computer Society Press: 29–38.

    Google Scholar 

  • Flotzinger, D., Kalcher, J., and Pfurtscheller, G. (1993). Suitability of Learning Vector Quantization for on-line learning: A case study of EEG classification. Proceedings of the World Conference on Neural Networks (WCCN - 93), Vol. 1. New Jersey: Lawrence Erlbaum Associates.

    Google Scholar 

  • Funahashi, K. (1989). On the approximate realization of continuous mappings by neural networks. Neural Networks, 2: 183–192.

    Article  Google Scholar 

  • Gabor, Andrew J. and Seyal, Masud (1992). Automated interictal EEG spike detection using artificial neural networks. Electroencephalography & Clinical Neurophysiology, 83(5): 271–280.

    Article  Google Scholar 

  • Grozinger, M., Kloppel, B., and Roschke, J. (1993). Recognition of rapid-eye movement (REM) sleep by artificial neural networks. Proceedings of the World Conference on Neural Networks (WCCN - 93), Vol. 1. New Jersey: Lawrence Erlbaum Associates.

    Google Scholar 

  • Hornik, K., Stinchcombe, M., and White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks,2(5): 359–366.

    Article  Google Scholar 

  • Jando, G., Siegel, R. M., Horvath, Z., and Buzaki, G. (1993). Pattern recognition of the electroencephalogram by artificial neural networks. Electroencephalography and Clinical Neurophysiology, 86: 100–109.

    Article  Google Scholar 

  • Kloppel, B. (1994a). Neural networks as a new method for EEG analysis. Pharmacoelectroencephalography, 29: 33–38.

    Google Scholar 

  • Kloppel, B. (1994b). Application of Neural Networks for EEG analysis. Pharmacoelectroencephalography, 29: 39–46.

    Google Scholar 

  • Kohonen, T. (1989). Self Organization and Associative Memory, 3rd Edition. New York: Springer-Verlag.

    Book  Google Scholar 

  • Lisboa, P. J. G., Mehridehnavi, A. R., and Martin, P. A. (1994). The interpretation of supervised neural networks. In P. J. G. Lisboa and M. J. Taylor (Eds.), Proceedings of the Workshop on Neural Network Applications and Tools, Los Alamitos, CA: IEEE Computer Society Press: 11–17.

    Google Scholar 

  • Lorenz, E. N. (1963). Deterministic non-periodic flow. Journal of Atmospheric Science, 20: 130–141.

    Article  Google Scholar 

  • Minsky, M. and Papert, S. (1969). Perceptrons. Cambridge, Mass.: MIT Press.

    MATH  Google Scholar 

  • Pfurtscheller, G., Flotzinger, D., Mohl, W., and Peltoranta, M. (1992). Prediction of the side of hand movements from single-trial multi channel EEG data using neural networks. Electroencephalography & Clinical Neurophysiology 82(4): 313–315.

    Article  Google Scholar 

  • Rosenblatt, Frank (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65: 386–408.

    Article  MathSciNet  Google Scholar 

  • McClelland, J. L., Rumelhart, D. E., and the PDP Research Group (1986). Parallel Distributed Processing: Explorations in the Microstructure of Cognition.Vols. 1 and 2. Cambridge, Mass.: MIT Press.

    Google Scholar 

  • Sietsma, Jocelyn; Dow, Robert J. (1991). Creating artificial neural networks that generalize. Neural Networks, 4(2): 67–79.

    Article  Google Scholar 

  • Silva, F. M. and Almeida, L. B. (1990). Acceleration techniques for the backpropagation algorithm. In L. B. Almeida and Wellekens (Eds.) Neural Networks, Europe Lecture Notes in Computer Science. Berlin: Springer-Verlag: 110–119.

    Chapter  Google Scholar 

  • Slater, J. D., Wu, F. Y., Honig, L. S., Ramsay, R. E., and Morgan, R. (1994). Neural network analysis of the P300 event-related potential in multiple sclerosis. Electroencephalography and Clinical Neurophysiology, 90: 114–122.

    Article  Google Scholar 

  • Thornton, C. J. (1992). Techniques in Computational Learning. London: Chapman and Hall.

    Google Scholar 

  • Vogl, T. P., Manglis, J. K., Rigler, A. K., Zink, W. T., and Alkon, D. L. (1988). Accelerating the convergence of the back-propagation method. Biological Cybernetics, 59: 257–263.

    Article  Google Scholar 

  • Webb, A. R. and Lowe, D. (1990). The optimized internal representation of multilayer classifier networks performs non-linear discriminant analysis. Neural Networks3(4): 367–375.

    Article  Google Scholar 

  • Werbos, P. J. (1974). Beyond regression: New tools for prediction and analysis in the behavioral sciences. Ph.D. dissertation, Harvard University, Cambridge, Mass.

    Google Scholar 

References

  • Parzen, E. (1962). On estimation of a probability density function and mode. Annals of Mathematical Statistics, 33, 1065–1076.

    Article  MathSciNet  MATH  Google Scholar 

  • Specht, D. F. (1991). A general regression neural network. IEEE Transaction on Neural Networks, 2(6), 568–576.

    Article  Google Scholar 

  • Wasserman, P. D. (1993). Advanced Methods in Neural Computing. New York: Van Nostrand Reinhold.

    MATH  Google Scholar 

Reference

  • Holmes, P. J. (1979). A nonlinear oscillator with a strange attractor. Philosophical Transactions of the Royal Society of London, A, 292: 419–448.

    Article  MATH  Google Scholar 

References

  • Albano, A. M., Muench, J., Schwartz, C., Mees, A. I., and Rapp, P. E. (1988). Singular-value decomposition and the Grassberger-Procaccia algorithm. Physics Review A, 38, 3017–3026.

    Article  MathSciNet  Google Scholar 

  • Badii, R., Broggi, G., Derighetti, B., Ravani, M., Ciliberto, S., Politi, A., and Rubio, M. A. (1988). Dimension increase in filtered chaotic signals. Physical Review Letters,60(11), 979–982.

    Article  Google Scholar 

  • Berliner, L. M. (1992). Statistics, probability and chaos. Statistical Science, 7(1), 69–90.

    Article  MathSciNet  MATH  Google Scholar 

  • Broomhead, D. S. and King, G. P. (1986). Extracting qualitative dynamics from experimental data. Physica 20D, 217–236.

    MathSciNet  Google Scholar 

  • Casdagli, M., Eubank, S., Farmer, J. D. (1991). State-space reconstruction in the presence of noise. Physica 51D, 352–359.

    MathSciNet  Google Scholar 

  • Chatterjee, S. and Yilmaz, M. R. (1992). Use of estimated fractal dimension in model identification for time series. Journal of Statistical Computation and Simulation, 41(3/4), 129–141.

    Article  Google Scholar 

  • Chatterjee, S. and Yilmaz, M. R. (1992). Chaos, fractals and statistics. Statistical Science, 7(1), 49–68.

    Article  MathSciNet  MATH  Google Scholar 

  • DeCoster, G. P. and Mitchell, D. W. (1991). The efficacy of the correlation dimension technique in detecting determinism in small samples. Journal of Statistical Computation and Simulation, 39(4), 221–229.

    Article  Google Scholar 

  • Destexhe, A., Sepulchre, J. A., and Babloyantz, A. (1988). A comparative study of the experimental quantification of deterministic chaos. Physics Letters A, 132, 101–106.

    Article  Google Scholar 

  • Ding, M., Grebogi, C., and Ott, E. (1989). Dimensions of strange nonchaotic attractors. Physics Letters A, 137(4/5), 167–172.

    Article  MathSciNet  Google Scholar 

  • Dvorak, I. and Siska, J. (1986). On some problems encountered in the estimation of the correlation dimension of the EEG. Physics Letters A, 118(2), 63–66.

    Article  Google Scholar 

  • Eckmann, J. P. and Ruelle, D. (1985). Ergodic theory of chaos and strange attractors. Review of Modern Physics, 57, 617–656.

    Article  MathSciNet  Google Scholar 

  • Essex, C. and Nerenberg, M. A. H. (1990). Correlation dimension and systematic geometric effects. Physics Review A, 42, 7065–7074.

    Article  MathSciNet  Google Scholar 

  • Essex, C. and Nerenberg, M. A. H. (1991). Comments on ‘Deterministic chaos: the science and the fiction’ by D. Ruelle. Proceedings of the Royal Society of London A, 435, 287–292.

    Article  MathSciNet  MATH  Google Scholar 

  • Farmer, J. D., Ott, E., and Yorke, J. A. (1983). The dimension of chaotic attractors. Physica 7D, 153–180.

    MathSciNet  Google Scholar 

  • Ford, J. (1987). Directions in classical chaos. In Hao Bai-lin (Ed.), Directions in Chaos. Singapore: World Scientific.

    Google Scholar 

  • Frank, G. W., Lookman, T., Nerenberg, M. A. H., Essex, C., Lemieux, J., and Blume, W. (1990). Chaotic time-series analyses of epileptic seizures. Physica 46D, 427–438.

    Google Scholar 

  • Fraser, A. M. and Swinney, H. L. (1986). Independent coordinates for strange attractors from mutual information. Physical Review A,33(2), 1134.

    Article  MathSciNet  MATH  Google Scholar 

  • Gibson, J. F., Farmer, J. D., Casdagli, M., and Eubank, S. (1992). An analytic approach to practical state-space reconstruction. Physica 57D, 1–30.

    MathSciNet  Google Scholar 

  • Grassberger, P. and Procaccia, I. (1983a). Characterization of strange attractors. Physical Review Letters, 50(5), 346–349.

    Article  MathSciNet  Google Scholar 

  • Grassberger, P. and Procaccia, I. (1983b). Measuring the strangeness of strange attractors. Physica 9D, 189–208.

    MathSciNet  Google Scholar 

  • Grassberger, P. (1986). Do climatic attractors exist? Nature, 323, 609–612.

    Article  Google Scholar 

  • Grebogi, C., Ott, E., Pelikan, S., and Yorke, J. A. (1984). Strange attractors that are not chaotic. Physica 13D, 261–268.

    MathSciNet  Google Scholar 

  • Hobbs, J. (1991). Chaos And Indeterminism. Canadian Journal of Philosophy, 21 (2), 141–164.

    Google Scholar 

  • Jackson, E. A. (1990). Perspectives of Nonlinear Dynamics, Vol. 2. Cambridge: Cambridge University Press.

    Book  MATH  Google Scholar 

  • Lefranc, M., Hennequin, D., and Glorieux, P. (1992). Improved correlation dimension estimates through changes of variable. Physics Letters A, 163 (4), 269–274.

    Article  Google Scholar 

  • Leibert, W. and Schuster, H. G. (1989). Proper choice of the time-delay for the analysis of chaotic time series. Physics Letters A, 142, 107–112.

    Article  MathSciNet  Google Scholar 

  • Martinerie, J. M., Albano, A. M., Mees, A. I., and Rapp, P. E. (1992). Mutual information, strange attractors, and the optimal estimation of dimension. Physical Review A, 45 (10), 7058–7064.

    Article  Google Scholar 

  • Moon, F. C. (1987). Chaotic Vibrations: An introduction for applied scientists and engineers. New York: John Wiley and Sons Ltd.

    MATH  Google Scholar 

  • Osborne, A. R. and Provenzale, A. (1989). Finite correlation dimension for stochastic systems with power-law spectra. Physica D, 35 (3), 357–381.

    Article  MathSciNet  MATH  Google Scholar 

  • Pritchard, W. S. and Duke, D. W. (1992). Dimensional analysis of no-task human EEG using the Grassberger-Procaccia method. Psychophysiology, 29 (2), 182–192.

    Article  Google Scholar 

  • Ramsey, J. B., Sayers, C. L., and Rothman, P. (1990). The statistical properties of dimension calculations using small data sets: some economic applications. International Economic Review, 31 (4), 991–1020.

    Article  Google Scholar 

  • Romeiras, F. J., Bondeson, A., Ott, E., Antonsen, T. M., and Grebogi, C. (1987). Quasiperiodically forced dynamical systems with strange nonchaotic attractors. Physica 26D, 277–294.

    MathSciNet  Google Scholar 

  • Smith, R. L. (1992). Comment: Relation between statistics and chaos. Statistical Science, 7 (1), 109–113.

    Article  Google Scholar 

  • Takens, F. (1980). Detecting strange attractors in turbulence. Dynamical Systems and Turbulence. Lecture Notes in Mathematics, 898, 366–381.

    Article  MathSciNet  Google Scholar 

References

  • Otnes, R. K. and Enochson, L. D. (1972). Digital Time Series Analysis. New York: John Wiley & Sons.

    MATH  Google Scholar 

  • Otnes, R. K. and Enochson, L. D. (1978). Applied Time Series Analysis. New York: John Wiley & Sons.

    MATH  Google Scholar 

References

  • Fraser, A. M. and Swinney, H. L. (1986). Independent coordinates for strange attractorsfrom mutual information. Physical Review A 33(2): 1130–1140.

    Article  MathSciNet  Google Scholar 

  • Gray, R. M. (1990). Entropy and Information Theory. New York: Springer-Verlag.

    Book  MATH  Google Scholar 

  • Shannon, Claude. E. (1948). A mathematical theory of communication. Bell System Technical Journal 27: 379–423.

    MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Brain-Behaviour Laboratory School of Applied Sciences, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada

    Edward J. Rzempoluck

Authors
  1. Edward J. Rzempoluck
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 1998 Springer Science+Business Media New York

About this chapter

Cite this chapter

Rzempoluck, E.J. (1998). Data Analysis. In: Neural Network Data Analysis Using Simulnet™. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-1746-6_3

Download citation

  • DOI: https://doi.org/10.1007/978-1-4612-1746-6_3

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4612-7262-5

  • Online ISBN: 978-1-4612-1746-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation