An efficient deep belief network with fuzzy learning for nonlinear system modeling

Gongming Wang, Junfei Qiao, Jing Bi, Mengchu Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A deep belief network (DBN) is one of the most effective ways to realize a deep learning technique, and has been attracting more and more attentions in nonlinear system modeling. However, it can not provide satisfactory results in learning speed and modeling accuracy, which is mainly caused by gradient diffusion. To address these problems and promote its development in cross-models, we propose an efficient DBN with a fuzzy neural network (DBFNN) for nonlinear system modeling. In this novel framework, DBN is considered as a pre-training technique to realize fast weight-initialization and to obtain a feature-representation vector. An FNN-based learning framework is developed for supervised modeling so as to eliminate the gradient diffusion issue, where its input happens to be the feature-representation vector. As a novel cross-model, DBFNN combines the advantages of both pre-training technique of DBN and an FNN model to improve nonlinear system modeling capability. A classical benchmark problem is used to demonstrate its superiority over existing single-models in learning speed and modeling accuracy.

Original languageEnglish (US)
Title of host publication2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3549-3554
Number of pages6
ISBN (Electronic)9781728145693
DOIs
StatePublished - Oct 2019
Event2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019 - Bari, Italy
Duration: Oct 6 2019Oct 9 2019

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Volume2019-October
ISSN (Print)1062-922X

Conference

Conference2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
CountryItaly
CityBari
Period10/6/1910/9/19

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Human-Computer Interaction

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