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Gout penyakit lama dihidapi manusia
Authors
JY Chang
MF Han
+3 more
SH Liao
CT Lin
YY Lin
Publication date
27 September 2011
Publisher
Doi
Cite
Abstract
This paper proposes a differential evolution with local information for TSK-type neuro-fuzzy system optimization. The differential evolution with local information consider neighborhood between each individual to keep the diversity of population. An adaptive parameter tuning based on 1/5th rule is used to trade off between local search and global search. For structure learning algorithm, the on-line clustering algorithm is used for rule generation. The structure learning algorithm generates a new rule which compares the firing strength. Initially, there is no rule in neuro-fuzzy system model. The rules are automatically generated by fuzzy measure. For parameter learning, the parameters are optimized by differential evolution algorithm. Finally, the proposed neuro-fuzzy system with novel differential evolution model is applied in chaotic sequence prediction problem. Results of this paper demonstrate the effectiveness of the proposed model. © 2011 IEEE
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Universiti Putra Malaysia Institutional Repository
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oai:psasir.upm.edu.my:67484
Last time updated on 30/04/2019
OPUS - University of Technology Sydney
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oai:opus.lib.uts.edu.au:10453/...
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Last time updated on 22/07/2021