Skip to main content
SpringerLink
Log in

Learning Speed in Neural Networks

  • Chapter
Learning and Coordination

Part of the book series: Microprocessor-Based and Intelligent Systems Engineering ((ISCA,volume 13))

  • 107 Accesses

Abstract

Intelligent systems should improve of their own accord over time. In the natural sphere, a proven technique for self-learning takes the form of neural networks.

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 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
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

  • Anderson, Dana Z. Neural Information Processing Systems. New York: American Institute of Physics, 1988.

    MATH  Google Scholar 

  • Anderson, J.A. and Rosenfeld, E. (eds) Neurocomputing. Cambridge, MA: MIT Press, 1988.

    Google Scholar 

  • Arbib, Michael A. and Hanson, Allen R., eds. Vision, Brain, and Cooperative Computation. Cambridge, MA: MIT Press, 1987.

    Google Scholar 

  • Borg-Grahm, Lyle J. “Simulations Suggest Information Processing Roles for the Diverse Currents in Hippocampal Neurons.” In Anderson (1988), pp.82–94.

    Google Scholar 

  • Carlson, Neil R. Physiology of Behavior,4th ed. Boston: Allyn and Bacon, 1991, ch. 6.

    Google Scholar 

  • Gefenstette, J.J. Proc. International Conference on Genetic Algorithms and their Applications. Carnegie-Mellon University, Pittsburgh, PA, July 1985.

    Google Scholar 

  • Geman, Stuart and Donald Geman. “Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images.” IEEE Trans. on Pattern Analysis and Machine Intelligence, v. 6(6), 1984, pp. 721–741.

    Article  MATH  Google Scholar 

  • Goldberg, D.E. Computer-aided gas pipeline operation using geneticalgorithms and rule learning. Ph.D. thesis, University of Michigan, 1983.

    Google Scholar 

  • Grossberg, S. “Adaptive Pattern Classification and Universal Recoding: Part I. Parallel Development and Coding of Neural Feature Detectors.” Biological Cybernetics,v. 23(3), 1976, pp. 121–134.

    Article  MathSciNet  MATH  Google Scholar 

  • Grossberg, S. “Classical and Instrumental Learning by Neural Networks.” Progress in Theoretical Biology, v. 3. NY: Academic Press, 1974, pp. 51–141.

    Google Scholar 

  • Haken, Herman, ed. Neural and Synergetic Computers. New York: Springer-Verlag, 1988.

    MATH  Google Scholar 

  • Hebb, D. O. The Organization of Behavior. NY: Wiley, 1949.

    Google Scholar 

  • Holland, J.H. Adaptation in Natural and Artificial Systems. Ann Arbor, MIUniv. Michigan Press, 1975

    Google Scholar 

  • Holland, J.H. Escaping brittleness: the possibilities of general-purpose learning algorithms applied to parallel rule-based systems. In R. S. Michalski et al., eds., Machine Learning: An Artificial Intelligence Approach,v. II, Los Altos, CA: M. Kaufmann, 1986: 593–624.

    Google Scholar 

  • Holland, J.H. and J.S. Reitman. Cognitive systems based on adaptive algorithms. In D.A. Waterman and F. Hayes-Roth, eds., Pattern-Directed Inference Systems, NY: Academic, 1978: pp. 313–329.

    Google Scholar 

  • Hopfield, J. J. “Neural Networks and Physical Systems with Emergent Collective Computational Abilities.” Proc. Nat. Acad. Sciences USA, v. 79(8), 1982, pp. 2554–2558.

    Article  MathSciNet  Google Scholar 

  • Livingstone, M. and D. Hubel. “Segregation of Form, Color, Movement and Depth: Anatomy, Physiology, and Perception.” Science, v. 240, 1988, pp. 740–749.

    Article  Google Scholar 

  • Kandel, Eric R. and James H. Schwartz. Principles of Neural Science. 2nd ed. NY: Elsevier, 1985, pp. 344–395.

    Google Scholar 

  • Kim, S.H. A mathematical framework for intelligent manufacturing systems. Proc. of Symposium on Integrated and Intelligent Manufacturing Systems, ASME, Anaheim, CA, Dec., 1986.

    Google Scholar 

  • Kim, S.H., “A Unified Framework for Self-Learning Systems”. Proc. Manufacturing International ‘88, v. III: Symp. on Manufacturing Systems, Atlanta, GA, April 1988: 165–170.

    Google Scholar 

  • Kim, S.H. Designing Intelligence. NY: Oxford University Press, 1990.

    Google Scholar 

  • Kirkpatrick, S., C. D. Gelatt Jr., and M. P. Vecchi. “Optimization by Simulated Annealing.” Science, v. 220(4598), 1983, pp. 671–680.

    Article  MathSciNet  MATH  Google Scholar 

  • Kohonen, T. Self-Organization and Associative Memory. NY: Springer-Verlag, 1984.

    MATH  Google Scholar 

  • Kompass, E.J. and T.J. Williams, eds. Learning Systems and Pattern Recognition. Barrington, IL: Technical Pub., 1983.

    Google Scholar 

  • Langley, P., G.L. Bradshaw, and H.A. Simon. Rediscovering chemistry with the BACON system. In R.S. Michalski, J.G. Carbonell and T.M. Mitchell, Machine Learning: An Artificial Intelligence Approach,Palo Alto, CA: Tioga, 1983: 307–329.

    Google Scholar 

  • Lenat, D. EURISKO: a program that learns new heuristics and design concepts: the nature of heuristics, III: program design and results. Artificial Intelligence, v.21(2), 1983: 61–98.

    Article  Google Scholar 

  • Mangel, Marc. “Evolutionary Optimization and Neural Network Models of Behavior.” Journal of Mathematical Biology, v.28 (3), 1990, pp.237–256.

    Article  MathSciNet  MATH  Google Scholar 

  • Minsky, Marvin and Seymour Papert. Perceptions: An Introduction to Computational Geometry. Cambridge, MA: M.I.T. Press, 1969; expanded ed., 1988.

    Google Scholar 

  • Nadel, Lynn, Cooper, Lynn A., Culicover, Peter, and Harnish, R. Michael, eds. Neural Connections, Mental Computation. Cambridge, MA: MIT Press, 1989.

    Google Scholar 

  • Pao, Y-H. “A Connectionist Net Approach to Autonomous Machine Learning of E Effective Process Control Strategies.” Robotics and Computer-Integrated Manufacturing, v.4 (3/4)1988, pp. 633–642.

    Article  Google Scholar 

  • Porter, Bruce W. and Mooney, Ray J., eds. Machine Learning: Proceedings of the Seventh International Conference on Machine Learning. Palo Alto, CA: Morgan Kaufmann, 1990.

    Google Scholar 

  • Rosenblatt, F. Principles of Neurodynamics. NY: Spartan, 1962.

    MATH  Google Scholar 

  • Rumelhart, D.E., G.E. Hinton and J.L. McClelland. “A General Framework for Parallel Distributed Processing.” In D.E. Rumelhart, J.L. McClelland and the PDP Research Group, Parallel Distributed Processing, v.1: Foundations, Cambridge, MA: M.I.T. Press, 1986, pp. 45–76.

    Google Scholar 

  • Sammut, Claude and Cribb, James. “Is Learning Rate a Good Performance Criterion for Learning?” In Porter and Mooney (1990), pp.170–178.

    Google Scholar 

  • Schmajuk, Nestor A. “Role of the Hippocampus in Temporal and Spatial Navigation: An Adaptive Neural Network.” Behavioral Brain Research, v. 39, 1990, pp.205–229.

    Article  Google Scholar 

  • Shibazaki, H. and S.H. Kim. “Learning Systems for Manufacturing Automation: Integrating Explicit and Implicit Knowledge.” Robotics and Computer-Integrated Manufacturing, v.9, 1992.

    Google Scholar 

  • Sutton, Richard S. and Andrew G. Barto. “Toward a Modem Theory of Adaptive Networks: Expectation and Prediction.” Psychological Review, v. 88(2), 1981, pp. 135–170.

    Article  Google Scholar 

  • Szu, Harold and Ralph Hartley. “Fast Simulated Annealing.” Physics Letters A, v. 122(3,4), 1987, pp. 157–162.

    Article  Google Scholar 

  • Tsypkin, Y.Z. Adaptation and Learning in Automatic Systems, trans. by Z.J. Nikolic.NY: Academic, 1971.

    Google Scholar 

  • Wasserman, Philip D. Neural Computing: Theory and Practice. NY: Van Nostrand Reinhold, 1989.

    Google Scholar 

  • Widrow, B. “Adaptive Sampled-Data Systems, A Statistical Theory of Adaptation.” in 1959 IRE WESCON Convention Record, part 4. NY: Institute of Radio Engineers, 1959.

    Google Scholar 

  • Widrow, B. and M. Hoff. “Adaptive Switching Circuits.” in 1960 IRE WESCON Convention Record. NY: Institute of Radio Engineers, 1960.

    Google Scholar 

  • Zeki, S.M. “The Cortical Projections of Foveal Striate Cortex in the Rhesus Monkey.” J. of Physiology, v.277, 1978, pp. 227–244.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Lightwell Inc., Charlottesville, Virginia, USA

    Steven H. Kim

Authors
  1. Steven H. Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 1994 Springer Science+Business Media Dordrecht

About this chapter

Cite this chapter

Kim, S.H. (1994). Learning Speed in Neural Networks. In: Learning and Coordination. Microprocessor-Based and Intelligent Systems Engineering, vol 13. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-1016-7_2

Download citation

  • DOI: https://doi.org/10.1007/978-94-011-1016-7_2

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-010-4442-4

  • Online ISBN: 978-94-011-1016-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation