New Methodology for Structure Identification of Fuzzy Controllers in Real Time
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
Hybridization of intelligent techniques and ARIMA models for time series prediction
Fuzzy Sets and Systems
Hierarchical Structure for function approximation using radial basis function
MATH'05 Proceedings of the 8th WSEAS International Conference on Applied Mathematics
TaSe, a Taylor series-based fuzzy system model that combines interpretability and accuracy
Fuzzy Sets and Systems
Local-global neuro-fuzzy system for color change modelling
International Journal of Hybrid Intelligent Systems - Advances in Intelligent Agent Systems
An algorithm for online self-organization of fuzzy controllers
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
Generating fuzzy models from deep knowledge: robustness and interpretability issues
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Analysis of the TaSe-II TSK-Type fuzzy system for function approximation
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Input variable selection in hierarchical RBF networks
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
A self-constructing compensatory neural fuzzy system and its applications
Mathematical and Computer Modelling: An International Journal
| Hi-index | 0.00 |
The identification of a model is one of the key issues in the field of fuzzy system modeling and function approximation theory. There are numerous approaches to the issue of parameter optimization within a fixed fuzzy system structure but no reliable method to obtain the optimal topology of the fuzzy system from a set of input-output data. This paper presents a reliable method to obtain the structure of a complete rule-based fuzzy system for a specific approximation accuracy of the training data, i.e., it can decide which input variables must be taken into account in the fuzzy system and how many membership functions (MFs) are needed in every selected input variable in order to reach the approximation target with the minimum number of parameters