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Evolving fuzzy optimally pruned extreme learning machine for regression problems

Content Provider	Springer Nature Link
Author	Pouzols, Federico Montesino Lendasse, Amaury
Copyright Year	2010
Abstract	This paper proposes an approach to the identification of evolving fuzzy Takagi–Sugeno systems based on the optimally pruned extreme learning machine (OP-ELM) methodology. First, we describe ELM, a simple yet accurate learning algorithm for training single-hidden layer feed-forward artificial neural networks with random hidden neurons. We then describe the OP-ELM methodology for building ELM models in a robust and simplified manner suitable for evolving approaches. Based on the previously proposed ELM method, and the OP-ELM methodology, we propose an identification method for self-developing or evolving neuro-fuzzy systems applicable to regression problems. This method, evolving fuzzy optimally pruned extreme learning machine (eF-OP-ELM), follows a random projection based approach to extracting evolving fuzzy rulebases. In this approach systems are not only evolving but their structure is defined on the basis of randomly generated fuzzy basis functions. A comparative analysis of eF-OP-ELM is performed over a diverse collection of benchmark datasets against well known evolving neuro-fuzzy methods, namely eTS and DENFIS. Results show that the method proposed yields compact rulebases, is robust and competitive in terms of accuracy.
Starting Page	43
Ending Page	58
Page Count	16
File Format	PDF
ISSN	18686478
Journal	Evolving Systems
Volume Number	1
Issue Number	1
e-ISSN	18686486
Language	English
Publisher	Springer-Verlag
Publisher Date	2010-07-07
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Evolving fuzzy systems Extreme learning machine Optimally pruned extreme learning machine Evolving Takagi–Sugeno Regression Time series Statistical Physics, Dynamical Systems and Complexity Artificial Intelligence (incl. Robotics) Complexity
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Control and Systems Engineering Modeling and Simulation Computer Science Applications

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