Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Flexible Neuro-fuzzy Systems: Structures, Learning and Performance Evaluation (Kluwer International Series in Engineering and Computer Science)
Automatic tuning of complex fuzzy systems with Xfuzzy
Fuzzy Sets and Systems
Advances in Fuzzy Clustering and its Applications
Advances in Fuzzy Clustering and its Applications
Methodology for long-term prediction of time series
Neurocomputing
Finding the embedding dimension and variable dependencies in time series
Neural Computation
OP-ELM: Theory, Experiments and a Toolbox
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Residual variance estimation in machine learning
Neurocomputing
Modified Gath-Geva fuzzy clustering for identification of Takagi-Sugeno fuzzy models
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 Systems, Man, and Cybernetics, Part A: Systems and Humans
Self-organized fuzzy system generation from training examples
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
The WM method completed: a flexible fuzzy system approach to data mining
IEEE Transactions on Fuzzy Systems
Neuro-fuzzy rule generation: survey in soft computing framework
IEEE Transactions on Neural Networks
A new clustering technique for function approximation
IEEE Transactions on Neural Networks
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We analyze the use of clustering methods for the automatic identification of fuzzy inference models for autoregressive prediction of time series. A methodology that combines fuzzy methods and residual variance estimation techniques is followed. A nonparametric residual variance estimator is used for a priori input and model selection. A simple scheme for initializing the widths of the input membership functions of fuzzy inference systems is proposed for the Improved Clustering for Function Approximation algorithm (ICFA), previously introduced for initializing RBF networks. This extension to the ICFA algorithm is shown to provide the most accurate predictions among a wide set of clustering algorithms. The method is applied to a diverse set of time series benchmarks. Its advantages in terms of accuracy and computational requirements are shown as compared to least-squares support vector machines (LS-SVM), the multilayer perceptron (MLP) and two variants of the extreme learning machine (ELM).