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Minimal optimal topologies for invariant higher-order neural architectures using genetic algorithms

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2 Author(s)
Liatsis, P. ; Control Syst. Centre, Univ. of Manchester Inst. of Sci. & Technol., UK ; Goulermas, Y.J.P.

Higher-order neural networks (HONNs) are successful in performing position, rotation and scale (PRSI) recognition. A major limitation of these networks is the combinatorial explosion of the higher-order terms, which increases the complexity of the network architecture. This work proposes a genetic optimisation scheme for determining the minimal optimal topology of a network for automated inspection of industrial parts

Published in:

Industrial Electronics, 1995. ISIE '95., Proceedings of the IEEE International Symposium on  (Volume:2 )

Date of Conference:

10-14 Jul1995