@inproceedings{
 title = {Design of experiments in neuro-fuzzy systems},
 type = {inproceedings},
 year = {2005},
 volume = {2005},
 id = {ceffd219-ac84-332e-8c1b-4a420d724246},
 created = {2019-02-14T18:02:01.422Z},
 file_attached = {false},
 profile_id = {74e7d4ea-3dac-3118-aab9-511a5b337e8f},
 last_modified = {2019-02-14T18:02:01.422Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {Interest in hybrid methods that combine artificial neural networks and fuzzy inference systems has grown in the last few years. These systems are robust solutions that search for representation of domain knowledge, reasoning on uncertainty, automatic learning and adaptation. However, the design and the definition of parameters effectiveness of these systems is a hard task yet. In this work we perform a statistical analysis to verify the interactions and interrelations between parameters in the design of neuro-fuzzy systems. The analysis carries out using a powerful statistical tool, the Design Of Experiments (DOE) in two neuro-fuzzy models, Adaptive Neuro Fuzzy Inference System (ANFIS) and Evolving Fuzzy Neural Networks (EFuNN). © 2005 IEEE.},
 bibtype = {inproceedings},
 author = {Zanchettin, C. and Minku, F.L. and Ludermir, T.B.},
 doi = {10.1109/ICHIS.2005.34},
 booktitle = {Proceedings - HIS 2005: Fifth International Conference on Hybrid Intelligent Systems}
}

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