Necessary first-person axioms of neuroconsciousness

Authors:
Igor Aleksander;Barry Dunmall
Affiliations:
Imperial College, London, UK;Imperial College, London, UK
Venue:
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
Year:
2003

Citing 4
Cited 1

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Generalized Analytic Rule Extraction for Feedforward Neural Networks

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Modeling Consciousness for Autonomous Robot Exploration

IWINAC '07 Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks

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Abstract

In this paper we propose a new algorithm for rule extraction from a trained Multilayer Feedforward network. The algorithm is based on an interval arithmetic network inversion for particular target outputs. The types of rules extracted are N-dimensional intervals in the input space. We have performed experiments with four databases and the results are very interesting. One rule extracted by the algorithm can cover 86% of the neural network output and in other cases 64 rules cover 100% of the neural network output.