A novel hybrid algorithm for function approximation
Expert Systems with Applications: An International Journal
Affine TS-model-based fuzzy regulating/servo control design
Fuzzy Sets and Systems
Robust neural-fuzzy method for function approximation
Expert Systems with Applications: An International Journal
Hybrid robust approach for TSK fuzzy modeling with outliers
Expert Systems with Applications: An International Journal
A new probabilistic fuzzy model: Fuzzification--Maximization (FM) approach
International Journal of Approximate Reasoning
Data-driven fuzzy modeling for Takagi-Sugeno-Kang fuzzy system
Information Sciences: an International Journal
Variational bayes for a mixed stochastic/deterministic fuzzy filter
IEEE Transactions on Fuzzy Systems
Identification of the linear parts of nonlinear systems for fuzzy modeling
Applied Soft Computing
Expert Systems with Applications: An International Journal
On maximum likelihood fuzzy neural networks
Fuzzy Sets and Systems
Robust TSK fuzzy modeling approach using noise clustering concept for function approximation
CIS'04 Proceedings of the First international conference on Computational and Information Science
A hierarchical procedure for the synthesis of ANFIS networks
Advances in Fuzzy Systems
Feature weighted unsupervised classification algorithm and adaptation for software cost estimation
International Journal of Computational Intelligence Studies
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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The Takagi-Sugeno-Kang (TSK) type of fuzzy models has attracted a great attention of the fuzzy modeling community due to their good performance in various applications. Most approaches for modeling TSK fuzzy rules define their fuzzy subspaces based on the idea of training data being close enough instead of having similar functions. Besides, training data sets algorithms often contain outliers, which seriously affect least-square error minimization clustering and learning algorithms. A robust TSK fuzzy modeling approach is presented. In the approach, a clustering algorithm termed as robust fuzzy regression agglomeration (RFRA) is proposed to define fuzzy subspaces in a fuzzy regression manner with robust capability against outliers. To obtain a more precision model, a robust fine-tuning algorithm is then employed. Various examples are used to verify the effectiveness of the proposed approach. From the simulation results, the proposed robust TSK fuzzy modeling indeed showed superior performance over other approaches