A new method for designing neuro-fuzzy systems for nonlinear modelling with interpretability aspects-Reference-Cited by-同舟云学术

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A new method for designing neuro-fuzzy systems for nonlinear modelling with interpretability aspects

Published:2014-07 Issue: Volume:135 Page:203-217
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Cpałka K.,Łapa K.,Przybył A.,Zalasiński M.

Funder

National Science Centre (Poland)

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference62 articles.

1. Interpretability-based fuzzy decision tree classifier a hybrid of the subtractive clustering and the multi-objective evolutionary algorithm;Afsari;Soft Comput.,2013

2. D. Aziz, M.A.M. Ali, K.B. Gan, I. Saiboon, Initialization of adaptive neuro-fuzzy inference system using fuzzy clustering in predicting primary triage category intelligent and advanced systems (ICIAS), in: 4th International Conference on Department of Electrical, Electronic & Systems Engineering, University of Kebangsaan, 2012, pp. 170–174.

3. Hybrid state variables-fuzzy logic modelling of nonlinear objects;Bartczuk;Artif. Intell. Soft Comput.,2013

4. A state-space modeling approach for the FPGA-based real-time simulation of high switching frequency power converters;Blanchette;IEEE Trans. Ind. Electron.,2012

5. Context adaptation of fuzzy systems through a multi-objective evolutionary approach based on a novel interpretability index;Botta;Soft Comput.,2009

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