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 algorithmā€¦Ā 
View on IEEE
dca.fee.unicamp.br
13 Citations
Highly Influential Citations
1
Background Citations
8
Methods Citations
3

Figures and Tables from this paper

13 Citations

Evolving Hybrid Neural Fuzzy Network for System Modeling and Time Series Forecasting

An evolving hybrid fuzzy neural network-based modeling approach using neurons based on uninorms and sigmoidal activation functions in a feed forward structure that is competitive when compared with representative methods of the current state of the art in evolving modeling.

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.

Prediction Intervals for Granular Data Streams Based on Evolving Type-2 Fuzzy Granular Neural Network Dynamic Ensemble

An interval type-2 (IT1) fuzzy granular neural network (FGNN) dynamic ensemble approach (IT2FGNNDEnsemble) is proposed, which can automatically generate, prune, merge, and realize recall in a single-pass learning mode for GDSs.

Evolving fuzzy linear regression tree approach for forecasting sales volume of petroleum products

Computational experiments show that the eFT predictor using LARS is an effective approach to nonlinear time series forecasting providing encouraging results in the competition scenario.

A Fuzzy Interval Time-Series Energy and Financial Forecasting Model Using Network-Based Multiple Time-Frequency Spaces and the Induced-Ordered Weighted Averaging Aggregation Operation

The proposed fuzzy interval time-series forecasting model on the basis of network-based multiple time-frequency spaces and the induced-ordered weighted averaging aggregation (IOWA) operation can achieve better performance than well-developed models.

Evolving Granular Systems

The approaches and framework introduced constitute a natural extension of evolving intelligent systems over numeric data streams to evolving granular systems over granular data streams and illustrate the usefulness of the granular approaches and frameworks proposed.

A new method for constructing granular neural networks based on rule extraction and extreme learning machine

Fault Detection and Diagnosis in dynamic systems using Weightless Neural Networks

Adaptive Granular Neural Networks for Remote Sensing Image Classification

  • D. A. KumarS. MeherK. Kumari
  • Environmental Science, Mathematics
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
  • 2018
A GNN with adaptive granular structure for the classification of remote sensing images that evolves according to the information present in the incoming labeled pixels is developed and improves the classification performance compared to other similar models.

Adaptive Granulation-Based Convolutional Neural Networks With Single Pass Learning for Remote Sensing Image Classification

  • S. PalDasari Arun Kumar
  • Computer Science
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
  • 2023
An adaptive granulation-based CNN (AGCNN) model that works in the framework of fuzzy set theoretic data granulation and adaptive learning by upgrading the network architecture to accommodate the information of new samples, and avoids iterative training is proposed.

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 byā€¦

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.

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.

Evolving Connectionist Systems: The Knowledge Engineering Approach

Evolving Connectionist Methods for Unsupervised Learning, Feature Selection, Model Creation, and Model Validation and Brain Inspired Evolving Connectionist Models.