A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Adaptive modelling, estimation and fusion from data: a neurofuzzy approach
Adaptive modelling, estimation and fusion from data: a neurofuzzy approach
Fuzzy local linearization and local basis function expansion innonlinear system modeling
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Design of adaptive fuzzy logic controller based on linguistic-hedgeconcepts and genetic algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Semantic constraints for membership function optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Improving the interpretability of TSK fuzzy models by combining global learning and local learning
IEEE Transactions on Fuzzy Systems
Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement
IEEE Transactions on Fuzzy Systems
Designing fuzzy inference systems from data: An interpretability-oriented review
IEEE Transactions on Fuzzy Systems
Compact and transparent fuzzy models and classifiers through iterative complexity reduction
IEEE Transactions on Fuzzy Systems
Multiobjective identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Fuzzy Systems
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Local model interpretability is a very important issue in neurofuzzy local linear models applied to nonlinear state estimation, process modelling and control. This paper proposes a new fuzzy membership function with desirable properties for improving the interpretability of neurofuzzy models. A learning algorithm for constructing neurofuzzy models based on this new membership function and a hybrid objective function is derived as well, which aims to achieve optimal balance between global model accuracy and local model interpretability. Experimental results have shown that the proposed approach is simple and effective in improving the interpretability of Takagi-Sugeno fuzzy models while preserving the model accuracy at a satisfactory level.