Neurofuzzy adaptive modelling and control
Neurofuzzy adaptive modelling and control
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Fuzzy Modeling for Control
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Hierarchical neuro-fuzzy quadtree models
Fuzzy Sets and Systems - Fuzzy models
IWANN '99 Proceedings of the International Work-Conference on Artificial and Natural Neural Networks: Foundations and Tools for Neural Modeling
Extracting Interpretable Fuzzy Rules from RBF Networks
Neural Processing Letters
Accuracy Improvements in Linguistic Fuzzy Modeling
Accuracy Improvements in Linguistic Fuzzy Modeling
Hybrid learning models to get the interpretability–accuracy trade-off in fuzzy modeling
Soft Computing - A Fusion of Foundations, Methodologies and Applications
International Journal of Approximate Reasoning
Output value-based initialization for radial basis function neural networks
Neural Processing Letters
Structure and parameter learning of neuro-fuzzy systems: A methodology and a comparative study
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Fast learning in networks of locally-tuned processing units
Neural Computation
An analysis of Ruspini partitions in Gödel logic
International Journal of Approximate Reasoning
TaSe, a Taylor series-based fuzzy system model that combines interpretability and accuracy
Fuzzy Sets and Systems
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
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
Rule-based modeling: precision and transparency
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Similarity measures in fuzzy rule base simplification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A systematic approach to a self-generating fuzzy rule-table forfunction approximation
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
Radial basis function networks, regression weights, and the expectation-maximization algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Forecasting time series with genetic fuzzy predictor ensemble
IEEE Transactions on Fuzzy Systems
A highly interpretable form of Sugeno inference systems
IEEE Transactions on Fuzzy Systems
Self-organized fuzzy system generation from training examples
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
Structure identification in complete rule-based fuzzy systems
IEEE Transactions on Fuzzy Systems
Multiobjective identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Fuzzy Systems
Effective optimization for fuzzy model predictive control
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Adaptive noise cancellation using enhanced dynamic fuzzy neural networks
IEEE Transactions on Fuzzy Systems
Design for Self-Organizing Fuzzy Neural Networks Based on Genetic Algorithms
IEEE Transactions on Fuzzy Systems
Automatic Design of Hierarchical Takagi–Sugeno Type Fuzzy Systems Using Evolutionary Algorithms
IEEE Transactions on Fuzzy Systems
A New Fuzzy Set Merging Technique Using Inclusion-Based Fuzzy Clustering
IEEE Transactions on Fuzzy Systems
Comparison of adaptive methods for function estimation from samples
IEEE Transactions on Neural Networks
A new clustering technique for function approximation
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Efficient learning algorithms for three-layer regular feedforward fuzzy neural networks
IEEE Transactions on Neural Networks
Functional equivalence between radial basis function networks and fuzzy inference systems
IEEE Transactions on Neural Networks
Signatures: Definitions, operators and applications to fuzzy modelling
Fuzzy Sets and Systems
New Online Self-Evolving Neuro Fuzzy controller based on the TaSe-NF model
Information Sciences: an International Journal
Local and global optimization for Takagi-Sugeno fuzzy system by memetic genetic programming
Expert Systems with Applications: An International Journal
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Optimization of the local sub-models when using neuro-fuzzy systems to model input/output data can be spoiled during the global optimization process, therefore special care has to be taken to avoid this loss. Some approaches involving grid input space partitioning that optimize local sub-models whilst still obtaining a good global system performance have appeared. However, this is a more complex task for radial basis function networks, scatter-partitioning fuzzy systems and neuro-fuzzy models in general. This work presents a modified neuro-fuzzy model, known as the TaSe-NF model, which is a special case of a scatter-partitioned Takagi-Sugeno-Kang fuzzy system or normalized radial basis function neural network. The TaSe-NF model maintains the optimization properties of the local sub-models (fuzzy rules or RBF nodes) when the model is globally optimized thanks to the modified calculation of the final normalized activation of the rules, which provides an appropriate input space partitioning to achieve these objectives. To show the characteristics of the proposed model, a learning methodology for this model, which consists of a clustering algorithm especially suited to function approximation problems, a local search technique and a membership function merging approach, with the objective of improving the transparency, i.e. ease of interpretability, of the extracted fuzzy rule set is also proposed.