An experiment in linguistic synthesis with a fuzzy logic controller
International Journal of Human-Computer Studies - Special issue: 1969-1999, the 30th anniversary
A Framework for Evolving Fuzzy Classifier Systems Using Genetic Programming
Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference
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Pattern Classification (2nd Edition)
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A Weighted Evolving Fuzzy Neural Network for Electricity Demand Forecasting
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Evolving takagi sugeno modelling with memory for slow processes
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IEEE Transactions on Fuzzy Systems
On-line incremental feature weighting in evolving fuzzy classifiers
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Handling drifts and shifts in on-line data streams with evolving fuzzy systems
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Evolving fuzzy neural networks for supervised/unsupervised onlineknowledge-based learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Fuzzy Systems
Identification of evolving fuzzy rule-based models
IEEE Transactions on Fuzzy Systems
Evolving Fuzzy-Rule-Based Classifiers From Data Streams
IEEE Transactions on Fuzzy Systems
FLEXFIS: A Robust Incremental Learning Approach for Evolving Takagi–Sugeno Fuzzy Models
IEEE Transactions on Fuzzy Systems
An Evolving Fuzzy Predictor for Industrial Applications
IEEE Transactions on Fuzzy Systems
Guest Editorial Evolving Fuzzy Systems–-Preface to the Special Section
IEEE Transactions on Fuzzy Systems
Multivariable Gaussian Evolving Fuzzy Modeling System
IEEE Transactions on Fuzzy Systems
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Information Sciences: an International Journal
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IEEE Transactions on Neural Networks
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This paper introduces an evolving neural fuzzy modeling approach constructed upon the neo-fuzzy neuron and network. The approach uses an incremental learning scheme to simultaneously granulate the input space and update the neural network weights. The neural network structure and parameters evolve simultaneously as data are input. Initially the space of each input variable is granulated using two complementary triangular membership functions. New triangular membership functions may be added, excluded and/or have their parameters adjusted depending on the input data and modeling error. The parameters of the network are updated using a gradient-based scheme with optimal learning rate. The performance of the approach is evaluated using instances of times series forecasting and nonlinear system identification problems. Computational experiments and comparisons against alternative evolving models show that the evolving neural neo-fuzzy network is accurate and fast, characteristics which are essential for adaptive systems modeling, especially in real-time, on-line environments.