A methodology to model fuzzy systems using fuzzy clustering in a rapid-prototyping approach
Fuzzy Sets and Systems
A clustering algorithm for fuzzy model identification
Fuzzy Sets and Systems
Neuro-fuzzy systems for function approximation
Fuzzy Sets and Systems - Special issue on analytical and structural considerations in fuzzy modeling
Fuzzy modeling with hybrid systems
Fuzzy Sets and Systems
Structure identification and parameter optimization for non-linear fuzzy modeling
Fuzzy Sets and Systems - Fuzzy systems
Clustering For Data Mining: A Data Recovery Approach (Chapman & Hall/Crc Computer Science)
Clustering For Data Mining: A Data Recovery Approach (Chapman & Hall/Crc Computer Science)
Recurrent neuro-fuzzy hybrid-learning approach to accurate system modeling
Fuzzy Sets and Systems
Modeling solid oxide fuel cells using continuous-time recurrent fuzzy systems
Engineering Applications of Artificial Intelligence
A recurrent fuzzy-neural model for dynamic system identification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Self-organized fuzzy system generation from training examples
IEEE Transactions on Fuzzy Systems
Generating an interpretable family of fuzzy partitions from data
IEEE Transactions on Fuzzy Systems
FLEXFIS: A Robust Incremental Learning Approach for Evolving Takagi–Sugeno Fuzzy Models
IEEE Transactions on Fuzzy Systems
A fuzzy-logic-based approach to qualitative modeling
IEEE Transactions on Fuzzy Systems
Recurrent neuro-fuzzy networks for nonlinear process modeling
IEEE Transactions on Neural Networks
A recurrent self-organizing neural fuzzy inference network
IEEE Transactions on Neural Networks
Modeling with discrete-time recurrent fuzzy systems via mixed-integer optimization
Fuzzy Sets and Systems
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Continuous-time recurrent fuzzy systems (CTRFS) allow the representation of knowledge-based dynamic processes that can be stated in the form of ''if ..., then ...'' rules. In this article we show how a CTRFS can not only be modeled by linguistically given knowledge but also by measured data. Furthermore, a unified approach for both structure and parameter identification of continuous-time recurrent fuzzy systems will be presented, resulting in a linguistically interpretable model of the considered dynamic process. The capability of the approach is shown by modeling of a biotechnological process.