Abstract
A universal node model is assumed in this general analysis of connectionist nets. It is based on a logic truth-table with a probabilistic element. It is argued that this covers other definitions. Algorithms are developed for training and testing techniques that involve reducing amounts of noise, giving a new perspective on annealing. The principle is further applied to ‘hard’ learning and shown to be achievable on the notorious parity-checking problem. The performance of the logic-probabilistic system is shown to be two orders of magnitude better than know back-error propagation techniques which have used this task as a benchmark.
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
Rumelhart D.E. and McClelland J.L.., (eds.) : Parellel Distributed Processing, Vol. 1 & Vol 2, MIT Press, Cambridge, Mass, 1986.
Aleksander I., Adaptive Visions Systems and Boltzmann Machines : a Rapprochement, Pattern Recognition Letters, Vol. 6 pp. 113–120, July 1987.
Hopfield J.J., : Neural Networks and Physical Systems with Emergent Computational Abilities, Proceedings of the National Academy of Sciences, U.S.A., Vol. 79, pp. 2554–2558, 1982.
Aleksander I., Thomas W.V., and Bowden P.A., : WISARD, a Radical Step Forward in Image Recognition, Sensor Review, vol. 4. no.3. pp. 120–124, 1984.
Aleksander I., : Brain Cell to Microcircuit, Electronics and Power, Vol. 16, pp. 48–51, 1970.
Rumelhart D.E., Hinton G.E., and Williams R.J., : Learning Internal Representations by Error Propagation, in Rumelhart D.E. and McClelland J.L., (eds.) : Parallel Distributed Processing, Vol. 1, MIT Press, Cambridge, Mass, 1986.
Minsky M. and Papert S., Perceptrons: an Introduction to Computational Geometry, MIT Press, Boston, 1969.
Hinton G.E., Sejnowski T.J., Ackley, D.H., : Boltzmann Machines: Constraint Satisfaction Networks that Learn, Tech. Rep. CMU CS 84 119, Carnegie Mellon University, Pittsburgh, 1984.
Kauffmann, S.A. : Metabolic Stability and Epigenesis in Randomly Constructed Genetic Nets, J. Theoret. Biol. Vol. 22 pp. 437–467, 1986.
Aleksander I. and Atlas, P., : Cyclic Activity in Nature: Causes of Stability, Int. J. of Neuroscience, Vol. 6, pp. 45–50, 1973.
Aleksander I., : The Logic of Connectionist Systems, Neural Net Research Report, Imperial College, London, August 1987.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1989 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Aleksander, I. (1989). Logical Connectionist Systems. In: Eckmiller, R., v.d. Malsburg, C. (eds) Neural Computers. Springer Study Edition, vol 41. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-83740-1_21
Download citation
DOI: https://doi.org/10.1007/978-3-642-83740-1_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-50892-2
Online ISBN: 978-3-642-83740-1
eBook Packages: Springer Book Archive