Neural Computing and Applications

, Volume 18, Issue 7, pp 707–717 | Cite as

A block-diagonal recurrent fuzzy neural network for system identification

Original Article

Abstract

A recurrent fuzzy neural network with internal feedback is suggested in this paper. The network is entitled dynamic block-diagonal fuzzy neural network (DBD-FNN), and constitutes a generalized Takagi-Sugeno-Kang fuzzy system, where the consequent parts of the fuzzy rules are small Block-Diagonal Recurrent Neural Networks. The proposed model is applied to a benchmark identification problem, where a dynamic system is to be identified. Additionally, an application of the proposed model to the problem of the analysis of lung sounds is presented. Particularly, a filter based on the DBD-FNN is developed, trained with the RENNCOM method. Extensive experimental and simulation results are given and performance comparisons with a series of other models are conducted, highlighting the modeling characteristics of DBD-FNN as an identification tool and the effectiveness of the proposed separation filter.

Keywords

Block-diagonal recurrent fuzzy-neural network Internal feedback System identification Separation of lung sounds 

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Copyright information

© Springer-Verlag London Limited 2008

Authors and Affiliations

  • Paris A. Mastorocostas
    • 1
  • Constantinos S. Hilas
    • 1
  1. 1.Department of Informatics and CommunicationsTechnological Educational Institute of SerresSerresGreece

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