Skip to main content
SpringerLink
Log in

Neural Architectures and Algorithms

  • Chapter
Applications of Neural Networks

  • 452 Accesses

Abstract

Artificial Neural Networks (ANNs) are intelligent, thinking machines. They work in the same way as the human brain. They learn from experience in a way that no conventional computer can and they will shortly solve all of the world’s hard computational problems.

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 189.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
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

  1. J. Hertz, A. Krogh, and R.G. Palmer, in Introduction to the Theory of Neural Computation, Addison-Wesley, 1991.

    Google Scholar 

  2. R.R Lippmann, “An Introduction to Computing with Neural Nets”, IEEE ASSP Magazine, pp. 4-22, April, 1987.

    Google Scholar 

  3. R.R Lippmann, “Pattern Classification using Neural Networks”, IEEE Communications Magazine, pp. 47-64, November, 1989.

    Google Scholar 

  4. E. Sanchez-Sinencio and C. Lau, in Artificial Neural Networks: Paradigms, Applications and Hardware Implementations, IEEE Press, 1991.

    Google Scholar 

  5. M.L. Minsky and S.A. Papert, Perceptrons: An Introduction to Computational Geometry, MIT Press, Cambridge, MA, 1969.

    MATH  Google Scholar 

  6. F. Rosenblatt, “The Perceptron: A Probabilistic Model for Information Storage and Organisation in the Brain”, Psychological Review, vol. 65, pp. 386–408, 1958.

    Article  MathSciNet  Google Scholar 

  7. J.J. Hopfield, “Neural Networks and Physical Systems with Emergent Collective Computational Abilities”, Proc. Natl. Acad. Sci. USA, vol. 79, pp. 2554–2558, April, 1982.

    Article  MathSciNet  ADS  Google Scholar 

  8. J.J. Hopfield, “Neural Networks and Physical Systems with Graded Response have Collective Properties like those of Two-State Neurons”, Proc. Natl. Acad. Sci. USA, vol. 81, pp. 3088–3092, May, 1984.

    Article  ADS  Google Scholar 

  9. D.E. Rumelhart and J.D. McLelland, in Parallel Distributed Processing: Explorations in the Microstructures of Cognition Volume 1, MIT Press, 1986.

    Google Scholar 

  10. A.F. Murray, “Multi-Layer Perceptron Learning Optimised for On-Chip Implementation — a Noise-Robust System”, Neural Computation, vol. 4, no. 3, pp. 366–381, 1992.

    Article  Google Scholar 

  11. R. Rohwer, “The “Moving Targets” Training Algorithm”, Neural Information Processing Systems (NIPS) Conference, pp. 558-565, Morgan Kaufmann, 1990.

    Google Scholar 

  12. D. Nabutovsky, T. Grossman, and E. Domany, “Learning by CHIR without Storing Internal Representations”, Complex Systems, vol. 4, pp. 519–541, 1990.

    MathSciNet  MATH  Google Scholar 

  13. M. Jabri and B. Flower, “Weight Perturbation: An Optimal Architecture and Learning Technique for Analog VLSI Feedforward and Recurrent Multilayer Networks”, Neural Computation, vol. 3, pp. 546–565, 1991.

    Article  Google Scholar 

  14. Y. Le Cun, J.S. Denker, and S. Solla, “Optimal Brain Damage”, Neural Information Processing Systems (NIPS) Conference, pp. 598-605, Morgan Kaufmann, 1990.

    Google Scholar 

  15. D.S. Broomhead and D. Lowe, “Multivariate Functional Interpolation and Adaptice Networks”, Complex Systems, vol. 2, pp. 321–355, 1988.

    MathSciNet  MATH  Google Scholar 

  16. J. Moody and C. Darken, “Fast learning in Networks of Locally-Tuned Processing Units”, Neural Computation, vol. 1, pp. 281–294, 1989.

    Article  Google Scholar 

  17. C. Darken and J. Moody, “Note on Learning Rate Schedules for Stochastic Optimisation”, Neural Information Processing Systems (NIPS) Conference, pp. 832-838, Morgan Kaufmann, 1991.

    Google Scholar 

  18. T. Kohonen, Self-organisation and Associative Memory, Springer-Verlag, Berlin, 1984.

    Google Scholar 

  19. D.E. Rumelhart, G.E. Hinton, and R.J. Williams, “Learning Internal Representations by Error Propagation”, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1, pp. 318–362, 1986.

    Google Scholar 

  20. H.J. Bremermann and R.W. Anderson, “An alternative to back-propagation: A simple rule for synaptic modification for neural net training and memory”, Internal Report, Univ. of California, Berkeley., 1989.

    Google Scholar 

  21. PJ. Werbos, “A menu for designs of reinforcement learning over time”, in Neural networks for control, ed. W.T. Miller III, R.S. Sutton, and P. J. Werbos, MIT Press, Cambridge, MA., 1990.

    Google Scholar 

  22. A.G. Barto and P. Anandan, “Pattern Learning Stochastic Learning Automata”, IEEE Trans Systems, Man and Cybernetics, vol. 15, pp. 360–375, 1985.

    Article  MathSciNet  MATH  Google Scholar 

  23. T. Prescott and J. Mayhew, “Obstacle Avoidance through Reinforcement Learning”, Neural Information Processing Systems (NIPS) Conference, pp. 523-530, 1991.

    Google Scholar 

  24. R.S. Sutton, “Learning to predict by methods of temporal difference”, Machine Learning, vol. 3, pp. 9–44, 1988.

    Google Scholar 

  25. B. Widrow and M.A. Lehr, “30 years of adaptive Neural networks: Perceptron, Madaline, and backpropagation”, Proc. IEEE, vol. 78, pp. 1415–1442, 1990.

    Article  Google Scholar 

  26. G.E. Hinton, T.J. Sejnowski, and D.H. Ackley, “Bolzmann Machines: Constraint Satisfaction Networks that Learn”, Cognitive Science, vol. 9, pp. 147–169, May, 1984.

    Google Scholar 

  27. D.G. Bounds, “Numerical Simulation of Boltzman Machines”, in AIP Conference Proceedings 151, Neural Networks for Computing, Snowbird, ed. John S. Denker, pp. 59-64, American Institute of Physics, 1986.

    Google Scholar 

  28. F. Zou and J. Nossek, “A Chaotic Attractor with Cellular Neural Networks”, IEEE Trans CAS, vol. 38, 1991.

    Google Scholar 

  29. H.P. Graf, L.D. Jackel, R.E. Howard, B. Straughn, J.S. Denker, W. Hubbard, D.M. Tennant, and D. Schwartz, “VLSI Implementation of a Neural Network Memory with Several Hundreds of Neurons”, Proc. AIP Conference on Neural Networks for Computing, Snowbird, pp. 182-187, 1986.

    Google Scholar 

  30. C. Mead, in Analog VLSI and Neural Systems, Addison-Wesley, 1988.

    Google Scholar 

  31. A.G. Andreou, K.A. Boahen, P.O. Pouliquen, A. Pavasovic, R.E. Junkins, and K. Strohbehn, “Current-Mode Subthreshold MOS Circuits for Analog VLSI Neural Systems”, IEEE Transactions Neural Networks, vol. 2, no. 2, pp. 205–213, 1991.

    Article  ADS  Google Scholar 

  32. L.A. Akers, M.R. Walker, D.K. Ferry, and R.O. Grondin, “A Limited-Interconnect, Highly Layered Synthetic Neural Architecture”, in VLSI for Artificial Intelligence, ed. J.G. Delgado-Frias and W.R. Moore, pp. 218-226, Kluwer, July 1988.

    Google Scholar 

  33. C.R. Schneider and H.C. Card, “Analog CMOS Contrastive Hebbian Networks”, Applications of Artificial Neural Networks III SPIE Proc, p. 1709, 1992.

    Google Scholar 

  34. A.F. Murray and A.V.W. Smith, “Asynchronous Arithmetic for VLSI Neural Systems”, Electronics Letters, vol. 23, no. 12, pp. 642–643, June, 1987.

    Article  Google Scholar 

  35. A.F. Murray, A. Hamilton, D.J. Baxter, S. Churcher, H.M. Reekie, and L. Tarassenko, “Integrated Pulse-Stream Neural Networks — Results, Issues and Pointers”, IEEE Trans. Neural Networks, pp. 385-393, 1992.

    Google Scholar 

  36. J. Meador, A. Wu, C. Cole, N. Nintunze, and P. Chintrakulchai, “Programmable Impulse Neural Circuits”, IEEE Transactions on Neural Networks, vol. 2, no. 1, pp. 101–109, 1990.

    Article  Google Scholar 

  37. L.M. Reyneri, F. Gregoreti, and C. Truzzi, “Interfacing Sensors and Actuators to CPWM and CPEM Neural Networks”, Proc. International Conference on Microelectronics for Neural Networks, Edinburgh, pp. 11-20, 1993.

    Google Scholar 

  38. J. Tomberg and K. Kaski, “Some IC Implementations of Artificial Neural Networks Using Synchronous Pulse-Density Modulation Technique”, Int. Journal of Neural Systems, vol. 2, no. 1/2, pp. 101–114, 1991.

    Article  Google Scholar 

  39. D. Hammerstrom, “A Highly Parallel Digital Architecture for Neural Network Emulation”, VLSI for AI and Neural Networks, pp. 357-366, Plenum, 1991.

    Google Scholar 

  40. U. Ramacher, W. Raab, J. Anlauf, U. Hachmann, J. Beichter, N. Bruls, R. Manner, J. Glas, and A. Wurz, “Multiprocessor and Memory Architecture of the Neurocomputer SYNAPSE-1”, Proc. International Conference on Microelectronics for Neural Networks, Edinburgh, pp. 227-232, 1993.

    Google Scholar 

  41. R.M. Goodman, P. Smyth, C.M. Higgins, and J.W. Miller, “Rule-Based Neural Networks for Classification and Probability Estimation”, Neural Computation, vol. 4, no. 6, pp. 781–804, 1992.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, University of Edinburgh, Edinburgh, EH9 3JL, UK

    Alan Murray

Authors
  1. Alan Murray
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. The University of Edinburgh, UK

    Alan F. Murray

Rights and permissions

Reprints and permissions

Copyright information

© 1995 Springer Science+Business Media New York

About this chapter

Cite this chapter

Murray, A. (1995). Neural Architectures and Algorithms. In: Murray, A.F. (eds) Applications of Neural Networks. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-2379-3_1

Download citation

  • DOI: https://doi.org/10.1007/978-1-4757-2379-3_1

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4419-5140-3

  • Online ISBN: 978-1-4757-2379-3

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation