Applications of artificial neural network based fuzzy inference system

  • Ernest Czogała
  • Jacek Łęski
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 47)


In previous chapters we introduced the artificial neural network based fuzzy inference system (ANNBFIS) network structure. The learning methods, clustering of input space, use of different fuzzy implications in inference process and other related topics are shown. In this chapter we will show several applications of ANNBFIS to solving many practical problems, as: time series prediction, signal compression, classifications of patterns, system identifications, control and equalization of digital communication channel. All above applications will be tested on benchmark data sets. These data can be easily obtained via Internet. This approach ensures easy comparison of the proposed system to systems known from literature, and the readers can compare their own systems to the system presented in this book.


Artificial Neural Network Fuzzy Inference System Cluster Validity Chaotic Time Series Average Data Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Physica-Verlag Heidelberg 2000

Authors and Affiliations

  • Ernest Czogała
    • 1
  • Jacek Łęski
    • 1
  1. 1.Institute of ElectronicsSilesian University of TechnologyGliwicePoland

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