Fuzzy Sets and Systems - Special issue on fuzzy neural control
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
A simply identified Sugeno-type fuzzy model via double clustering
Information Sciences—Informatics and Computer Science: An International Journal - Special issue on modeling with soft-computing
Reconstruction and representation of 3D objects with radial basis functions
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Genetic programming for model selection of TSK-fuzzy systems
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Structure identification and parameter optimization for non-linear fuzzy modeling
Fuzzy Sets and Systems - Fuzzy systems
Toward a generalized theory of uncertainty (GTU): an outline
Information Sciences—Informatics and Computer Science: An International Journal
International Journal of Approximate Reasoning
Comparison of different strategies of utilizing fuzzy clustering in structure identification
Information Sciences: an International Journal
Application of evolving Takagi-Sugeno fuzzy model to nonlinear system identification
Applied Soft Computing
Is there a need for fuzzy logic?
Information Sciences: an International Journal
On the use of the weighted fuzzy c-means in fuzzy modeling
Advances in Engineering Software
Agent-based evolutionary approach for interpretable rule-based knowledge extraction
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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
A TSK-type neurofuzzy network approach to system modeling problems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolving Compact and Interpretable Takagi–Sugeno Fuzzy Models With a New Encoding Scheme
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Fuzzy Systems
A new approach to fuzzy modeling
IEEE Transactions on Fuzzy Systems
A transformed input-domain approach to fuzzy modeling
IEEE Transactions on Fuzzy Systems
Implementation of evolutionary fuzzy systems
IEEE Transactions on Fuzzy Systems
Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement
IEEE Transactions on Fuzzy Systems
GA-fuzzy modeling and classification: complexity and performance
IEEE Transactions on Fuzzy Systems
Compact and transparent fuzzy models and classifiers through iterative complexity reduction
IEEE Transactions on Fuzzy Systems
Generating the knowledge base of a fuzzy rule-based system by the genetic learning of the data base
IEEE Transactions on Fuzzy Systems
Robust TSK fuzzy modeling for function approximation with outliers
IEEE Transactions on Fuzzy Systems
Linguistic modeling by hierarchical systems of linguistic rules
IEEE Transactions on Fuzzy Systems
Identification of evolving fuzzy rule-based models
IEEE Transactions on Fuzzy Systems
Data-driven linguistic modeling using relational fuzzy rules
IEEE Transactions on Fuzzy Systems
A neuro-fuzzy system modeling with self-constructing rule generationand hybrid SVD-based learning
IEEE Transactions on Fuzzy Systems
A fuzzy-logic-based approach to qualitative modeling
IEEE Transactions on Fuzzy Systems
Neuro-fuzzy rule generation: survey in soft computing framework
IEEE Transactions on Neural Networks
So near and yet so far: New insight into properties of some well-known classifier paradigms
Information Sciences: an International Journal
Information Sciences: an International Journal
Data driven generation of fuzzy systems: an application to breast cancer detection
CIBB'10 Proceedings of the 7th international conference on Computational intelligence methods for bioinformatics and biostatistics
Granular instances selection for fuzzy modeling
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Knowledge discovery by an intelligent approach using complex fuzzy sets
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part I
Linguistic fuzzy model identification based on PSO with different length of particles
Applied Soft Computing
Information Sciences: an International Journal
Local model network identification for online engine modelling
Information Sciences: an International Journal
Online extraction of main linear trends for nonlinear time-varying processes
Information Sciences: an International Journal
Fuzzy adaptive synchronization of time-reversed chaotic systems via a new adaptive control strategy
Information Sciences: an International Journal
Data driven system identification using evolutionary algorithms
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
A new indirect approach to the type-2 fuzzy systems modeling and design
Information Sciences: an International Journal
On the inference and approximation properties of belief rule based systems
Information Sciences: an International Journal
Data-based stability analysis of a class of nonlinear discrete-time systems
Information Sciences: an International Journal
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This paper presents a systematic approach to design first order Tagaki-Sugeno-Kang (TSK) fuzzy systems. This approach attempts to obtain the fuzzy rules without any assumption about the structure of the data. The structure identification and parameter optimization steps in this approach are carried out automatically, and are capable of finding the optimal number of the rules with an acceptable accuracy. Starting with an initial structure, the system first tries to improve the structure and, then, as soon as an improved structure is found, it fine tunes its rules' parameters. Then, it goes back to improve the structure again to find a better structure and re-fine tune the rules' parameters. This loop continues until a satisfactory solution (TSK model) is found. The proposed approach has successfully been applied to well-known benchmark datasets and real-world problems. The obtained results are compared with those obtained with other methods from the literature. Experimental studies demonstrate that the predicted properties have a good agreement with the measured data by using the elicited fuzzy model with a small number of rules. Finally, as a case study, the proposed approach is applied to the desulfurization process of a real steel industry. Comparing the proposed approach with some other fuzzy systems and neural networks, it is shown that the developed TSK fuzzy system exhibits better results with higher accuracy and smaller size of architecture.