Recursive estimation and time-series analysis: an introduction
Recursive estimation and time-series analysis: an introduction
A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
Lectures & Adaptive Parameter Estimation
Lectures & Adaptive Parameter Estimation
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Evolving rule-based models: a tool for design of flexible adaptive systems
Evolving rule-based models: a tool for design of flexible adaptive systems
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Knowledge-Based Clustering: From Data to Information Granules
Knowledge-Based Clustering: From Data to Information Granules
Data Mining and Knowledge Discovery Handbook
Data Mining and Knowledge Discovery Handbook
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Evolving Connectionist Systems: The Knowledge Engineering Approach
Evolving Connectionist Systems: The Knowledge Engineering Approach
Learning from Imprecise Granular Data Using Trapezoidal Fuzzy Set Representations
SUM '07 Proceedings of the 1st international conference on Scalable Uncertainty Management
Handbook of Granular Computing
Handbook of Granular Computing
Fuzzy Systems Engineering: Toward Human-Centric Computing
Fuzzy Systems Engineering: Toward Human-Centric Computing
Aggregation Functions: A Guide for Practitioners
Aggregation Functions: A Guide for Practitioners
Generalized theory of uncertainty (GTU)-principal concepts and ideas
Computational Statistics & Data Analysis
Participatory learning with granular observations
IEEE Transactions on Fuzzy Systems
A granular reflex fuzzy min-max neural network for classification
IEEE Transactions on Neural Networks
Fuzzy reasoning model under quotient space structure
Information Sciences: an International Journal
On-line incremental feature weighting in evolving fuzzy classifiers
Fuzzy Sets and Systems
Data Mining: Practical Machine Learning Tools and Techniques
Data Mining: Practical Machine Learning Tools and Techniques
Editorial: Special issue on interpretable fuzzy systems
Information Sciences: an International Journal
Evolving fuzzy neural networks for supervised/unsupervised onlineknowledge-based learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An approach to online identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Fuzzy Systems
Evolving Fuzzy-Rule-Based Classifiers From Data Streams
IEEE Transactions on Fuzzy Systems
Fuzzy min-max neural networks -- Part 2: Clustering
IEEE Transactions on Fuzzy Systems
Granular neural networks for numerical-linguistic data fusion and knowledge discovery
IEEE Transactions on Neural Networks
General fuzzy min-max neural network for clustering and classification
IEEE Transactions on Neural Networks
Heterogeneous fuzzy logic networks: fundamentals and development studies
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Fuzzy min-max neural networks. I. Classification
IEEE Transactions on Neural Networks
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This paper introduces a granular neural network framework for evolving fuzzy system modeling from fuzzy data streams. The evolving granular neural network (eGNN) is able to handle gradual and abrupt parameter changes typical of nonstationary (online) environments. eGNN builds interpretable multi-sized local models using fuzzy neurons for information fusion. An online incremental learning algorithm develops the neural network structure from the information contained in data streams. We focus on trapezoidal fuzzy intervals and objects with trapezoidal membership function representation. More precisely, the framework considers triangular, interval, and numeric types of data to construct granular fuzzy models as particular arrangements of trapezoids. Application examples in classification and function approximation in material and biomedical engineering are used to evaluate and illustrate the neural network usefulness. Simulation results suggest that the eGNN fuzzy modeling approach can handle fuzzy data successfully and outperforms alternative state-of-the-art approaches in terms of accuracy, transparency and compactness.