Skip to main content

Design of Neural Networks

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4692))

Abstract

The paper offers a critical analysis of the procedure observed in many applications of neural networks. Given a problem to be solved, a favorite NN-architecture is chosen and its parameters tuned with some standard training algorithm, but without taking in consideration relevant features of the problem or possibly its interdisciplinary nature. Three relevant benchmark problems are discussed to illustrate the thesis that “brute force solving is not the same as understanding”.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aizenberg, I.: Solving the parity n problem and other nonlinearly separable problems using a single universal binary neuron. Computational Intelligence. In: Reusch, B. (ed.) Theory and Applications, Springer, Berlin (2006)

    Google Scholar 

  2. Aizenberg, I., Moraga, C.: Multilayer feedforward neural network based on multi-valued neurons (MLMVN) and a backpropagation learning algorithm. Soft Computing 11(2), 169–183 (2007)

    Article  Google Scholar 

  3. Alvarez-Sánchez, J.R.: Injecting knowledge into the solution of the two-spiral problem. Neural Computing and Applications 8, 265–272 (1999)

    Article  Google Scholar 

  4. Allende, H., Moraga, C., Salas, R.: Artificial neural networks in forecasting: A comparative analysis. Kybernetika 38(6), 685–707 (2002)

    MathSciNet  Google Scholar 

  5. Box, G.E., Jenkins, G.M.: Time series analysis: forecasting and control, Holden-Day, Oakland CA, USA (1976)

    Google Scholar 

  6. Chow, T.W.S., Leung, C.T.: Nonlinear autoregressive integrated neural network model for short-term load forecasting. IEE Proc. Gener. Transm. Distrib. 143(5), 500–506 (1996)

    Article  Google Scholar 

  7. Durbin, R., Rumelhart, D.: Product units: A computationally powerful and biologically plausible extension to backpropagation networks. Neural Computation 1, 133–142 (1989)

    Google Scholar 

  8. Fahlman, S.E., Lebiere, C.: The Cascade Correlation Learning Architecture. In: Touretzky, S. (ed.) Advances in Neural Information Processing Systems, Morgan Kaufmann, San Francisco (1990)

    Google Scholar 

  9. Funahashi, K.I.: On the approximate realization of continuous mappings by neural networks. Neural Networks 2(3), 183–192 (1989)

    Article  Google Scholar 

  10. Han, J., Moraga, C.: The Influence of the Sigmoid Function Parameters on the Speed of Backpropagation Learning. In: Sandoval, F., Mira, J. (eds.) From Natural to Artificial Neural Computation. LNCS, vol. 930, pp. 195–201. Springer, Heidelberg (1995)

    Google Scholar 

  11. Hartman, E.J., Keeler, J.D., Kowalski, J.M.: Layered neural networks with Gaussian hidden units as universal approximators. Neural Computation 2, 210–215 (1990)

    Article  Google Scholar 

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

    Article  Google Scholar 

  13. Igel, Ch., Huesken, M.: Improving the Rprop Learning Algorithm. In: Proc. 2nd Int. Symposium on Neural Computation, pp. 115–121. Academic Press, London (2000)

    Google Scholar 

  14. Lang, K.J., Witbrock, M.J.: Learning to tell two spirals apart. In: Proceedings of the 1988 Connectionist Models Summer School, Morgan Kaufmann, San Francisco (1988)

    Google Scholar 

  15. Leerink, L.R., Giles, C.L., Horne, B.G., Marwan, A.J.: Learning with product units. Advances in Neural Information Processing. NIPS-94, 537–544 (1994)

    Google Scholar 

  16. Martin, R.D., Smarov, A., Vandaele, W.: Robust methods for ARIMA models. In: Zellner, A. (ed.) Proc. Conf. applied time series analysis of economic data, ASA-Census-NBER, pp. 153–169 (1983)

    Google Scholar 

  17. Mizutani, E., Dreyfus, S.E.: MLP’s hidden-node saturations and insensitivity to initial weights in two classification benchmark problems: parity and two spirals. In: Proc. IEEE Intl. Joint Conf. on Neural Networks, pp. 2831–2836. IEEE Computer Society Press, Los Alamitos (2002)

    Google Scholar 

  18. Minor, J.M.: Parity with two layer feedforward net. Neural Networks 6, 705–707 (1993)

    Article  Google Scholar 

  19. Moraga, C., Han, J.: Problem Solving =/= Problem understanding. In: Proceedings XVI International Conference of the Chilean Computer Science Society, pp. 22–30. SCCC–Press, Santiago (1996)

    Google Scholar 

  20. Müller, K.-R., Mika, S., Rätsch, G., Tsuda, K., Schölkopf, B.: An introduction to Kernel-based learning algorithms. In: Hu, Y.H., Hwang, Y.-N. (eds.) Chapter 4 of Handbook of Neural Networks Signal Processing, CRC-Press, Boca Raton, USA (2002)

    Google Scholar 

  21. Müller, K.-R., Smola, A., Rätsch, G., Schölkopf, B., Kohlmorgen, J., Vapnik, V.: Using Support Vector Machines for Time Series Prediction. In: Gerstner, W., Hasler, M., Germond, A., Nicoud, J.-D. (eds.) ICANN 1997. LNCS, vol. 1327, pp. 999–1004. Springer, Heidelberg (1997)

    Chapter  Google Scholar 

  22. Rumelhart, D.E., McClelland, J.L.: Parallel Distributed Processing: Explorations in the Microstructure of Cognition. MIT Press, Cambridge (1986)

    Google Scholar 

  23. Salas, R.: Private communication, 2002 and 2007

    Google Scholar 

  24. Sontag, E.D.: Feedforward nets for interpolation and classification. Jr. Comput. Systems Science 45, 20–48 (1992)

    Article  MATH  MathSciNet  Google Scholar 

  25. Stork, D.G., Allen, J.D.: How to solve the N-bit parity problem with two hidden units. Neural Networks 5, 923–926 (1992)

    Article  Google Scholar 

  26. Wieland, A.: Two spirals. CMU Repository of Neural Network Benchmarks (1988), http://www.bolz.cs.cmu.edu/benchmarks/two-spirals.html

Download references

Author information

Authors and Affiliations

Authors

Editor information

Bruno Apolloni Robert J. Howlett Lakhmi Jain

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Moraga, C. (2007). Design of Neural Networks. In: Apolloni, B., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2007. Lecture Notes in Computer Science(), vol 4692. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74819-9_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-74819-9_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74817-5

  • Online ISBN: 978-3-540-74819-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics