Fuzzy models, modular networks, and hybrid learning
Fuzzy Sets and Systems
Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Online learning in radial basis function networks
Neural Computation
A fast learning algorithm for parismonious fuzzy neural systems
Fuzzy Sets and Systems - Information processing
On-line fuzzy modeling via clustering and support vector machines
Information Sciences: an International Journal
On-Line T-S Fuzzy Model Identification with Growing and Pruning Rules
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Online fuzzy identification for an intelligent controller based on a simple platform
Engineering Applications of Artificial Intelligence
A decade of Kasabov's evolving connectionist systems: a review
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Online identification of a neuro-fuzzy model through indirect fuzzy clustering of data space
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Expert Systems with Applications: An International Journal
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
Identification of evolving fuzzy rule-based models
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
Hi-index | 12.05 |
In this paper, a systematic design of habitually evolving Takagi-Sugeno (TS) fuzzy systems, suggested for online prediction of processes with uncertainty, is introduced. A Habitually Linear Evolving Fuzzy System (HLEFS) starts off with an adaptive linear model and evolves into a TS fuzzy model whenever the linear model is unable to mitigate the output error. The number of rules in the HLEFS is controlled by an adaptive threshold on the error. The structure of the HLEFS tends to return to the adaptive linear model as soon as possible, and that is why we have dubbed the proposed model 'Habitually' Linear. Three theorems are stated and proved in a sequence in support of the HLEFS ability to keep the output error in a relatively small bound. It is shown that the adaptive linear model may not be good enough when the process changes abruptly and nonlinearly-what we call a Transient Significant Disturbance. In this case, it is proved that evolving into a TS fuzzy system with the proposed algorithm can mitigate the error. The performance of HLEFS in forecasting of daily electrical power consumption is studied and compared with that of four famous existing evolving fuzzy systems. Obtained results demonstrate the applicability and effectiveness of the proposed method in keeping the prediction error low with less number of fuzzy rules.