Evolving granular neural network for fuzzy time series forecasting

@article{Leite2012EvolvingGN,
  title={Evolving granular neural network for fuzzy time series forecasting},
  author={Daniel F. Leite and Pyramo Pires da Costa and F. Gomide},
  journal={The 2012 International Joint Conference on Neural Networks (IJCNN)},
  year={2012},
  pages={1-8}
}
  • D. LeiteP. CostaF. Gomide
  • Published 10 June 2012
  • Computer Science
  • The 2012 International Joint Conference on Neural Networks (IJCNN)
A primary requirement of a broad class of evolving intelligent systems is to process a sequence of numeric data over time. This paper introduces a granular neural network framework for evolving fuzzy system modeling from fuzzy data streams. The evolving granular neural network (eGNN) efficiently handles concept changes, distinctive events of nonstationary environments. eGNN constructs interpretable multi-sized local models using fuzzy neural information fusion. An incremental learning algorithm… 

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References

SHOWING 1-10 OF 34 REFERENCES

Evolving granular neural networks from fuzzy data streams

Evolving fuzzy granular modeling from nonstationary fuzzy data streams

Light is shed into the role of evolving fuzzy granular computing in providing high-quality approximate solutions from large volumes of real-world online data streams with gradual and abrupt regime shifts in the realm of the weather temperature prediction.

Evolving granular neural network for semi-supervised data stream classification

An adaptive fuzzy neural network framework for classification of data stream using a partially supervised learning algorithm that is robust against different types of concept drift, and is able to handle unlabeled examples efficiently.

Interval Approach for Evolving Granular System Modeling

This work considers interval granular objects to accommodate essential information from data streams and simplify complex real-world problems by recursively adapts both parameters and structure of rule-based models.

Evolving Fuzzy Systems from Data Streams in Real-Time

An approach to real-time generation of fuzzy rule-base systems of extended Takagi-Sugeno (xTS) type from data streams is proposed in the paper, which leads to a very powerful construct - evolving xTS (exTS).

An approach to online identification of Takagi-Sugeno fuzzy models

  • P. AngelovDimitar Filev
  • Computer Science
    IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
  • 2004
An approach to the online learning of Takagi-Sugeno (TS) type models is proposed in the paper. It is based on a novel learning algorithm that recursively updates TS model structure and parameters by

Granular neural networks

This article summarizes the recent research development of FNNs and RNNs (together called granular neural networks), which aims to process the massive volume of uncertain information, which is widespread applied in the authors' life.

Learning from Imprecise Granular Data Using Trapezoidal Fuzzy Set Representations

Throughout this work particular emphasis is placed on the simplicity of working with trapezoids while still retaining a rich representational capability.

Heterogeneous fuzzy logic networks: fundamentals and development studies

  • W. Pedrycz
  • Computer Science
    IEEE Transactions on Neural Networks
  • 2004
This study revisits the existing categories of logic neurons, provides with their taxonomy, helps understand their functional features and sheds light on their behavior when being treated as computational components of any neurofuzzy architecture.

Evolving Intelligent Systems: Methodology and Applications

Evolving Intelligent Systems is the one-stop reference guide for both theoretical and practical issues for computer scientists, engineers, researchers, applied mathematicians, machine learning and data mining experts, graduate students, and professionals.