A self-organizing interval Type-2 fuzzy-neural-network for modeling nonlinear systems-Reference-Cited by-同舟云学术

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A self-organizing interval Type-2 fuzzy-neural-network for modeling nonlinear systems

Published:2018-05 Issue: Volume:290 Page:196-207
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Han Hong-Gui,Chen Zhi-Yuan,Liu Hong-Xu,Qiao Jun-Fei

Funder

National Science Foundation of China

Beijing Natural Science Foundation

Major National Science and Technology

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference44 articles.

1. Ultra-orthogonal forward regression algorithms for the identification of non-linear dynamic systems;Guo;Neurocomputing,2016

2. An adaptive second order fuzzy neural network for nonlinear system modeling;Han;Neurocomputing,2016

3. Modeling of nonlinear system based on deep learning framework;Jin;Nonlinear Dyn.,2016

4. Self-adaptive robust nonlinear regression for unknown noise via mixture of Gaussians;Wang;Neurocomputing,2017

5. Adaptive KPCA modeling of nonlinear systems;Li;IEEE Trans. Signal Process.,2015

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