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Structural simplification of FMLP

descriptionPublicationkeyboard_double_arrow_right Article 16 Aug 2005Publisher:IEEEJournal:Proceedings. 2005 International Conference on Communications, Circuits and Systems, 2005., volume 2, pages 949-952
Authors: null Pangju Ai;

doi: 10.1109/icccas.2005.1495265

Structural simplification of FMLP

Abstract

The number of fuzzy rules directly determines the complexity and efficiency of a fuzzy multilayer perceptron (FMLP). Based on the neural network self-configuring learning (NNSCL) algorithm, the NNSCL-I algorithm is obtained by using the generalized inverse matrix (GIM) algorithm to adjust the remaining weights after pruning neurons. The NNSCL-I algorithm is applied in the rule-reasoning layer of the FMLP to simplify its rules and structure with no degradation in the original performance. Experimental results show the effectiveness and the feasibility of the algorithm.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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