Structure identification of fuzzy model
Fuzzy Sets and Systems
Neural-Network-Based Fuzzy Logic Control and Decision System
IEEE Transactions on Computers - Special issue on artificial neural networks
Neural networks in designing fuzzy systems for real world applications
Fuzzy Sets and Systems
Simplifying a neuro-fuzzy model
Neural Processing Letters
A neuro-fuzzy method to learn fuzzy classification rules from data
Fuzzy Sets and Systems - Special issue: application of neuro-fuzzy systems
Now comes the time to defuzzify neuro-fuzzy models
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Simplifying fuzzy modeling by both gray relational analysis and data transformation methods
Fuzzy Sets and Systems
Towards neuro-linguistic modeling: constraints for optimization of membership functions
Fuzzy Sets and Systems
Extraction of linguistic rules from data via neural networks and fuzzy approximation
Knowledge-based neurocomputing
Statistical Control of RBF-like Networks for Classification
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Gradient-Based Optimization of Hyperparameters
Neural Computation
A neuro-fuzzy based forecasting approach for rush order control applications
Expert Systems with Applications: An International Journal
Interpretability constraints for fuzzy information granulation
Information Sciences: an International Journal
Pattern recognition using neural-fuzzy networks based on improved particle swam optimization
Expert Systems with Applications: An International Journal
Logic-based fuzzy networks: A study in system modeling with triangular norms and uninorms
Fuzzy Sets and Systems
A Takagi-Sugeno type neuro-fuzzy network for determining child anemia
Expert Systems with Applications: An International Journal
Interpretability assessment of fuzzy knowledge bases: A cointension based approach
International Journal of Approximate Reasoning
Design of fuzzy rule-based classifiers with semantic cointension
Information Sciences: an International Journal
Review Article: Applications of neuro fuzzy systems: A brief review and future outline
Applied Soft Computing
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Neuro-fuzzy networks have been successfully applied to extract knowledge from data in the form of fuzzy rules. However, one drawback with the neuro-fuzzy approach is that the fuzzy rules induced by the learning process are not necessarily understandable. The lack of readability is essentially due to the high dimensionality of the parameter space that leads to excessive flexibility in the modification of parameters during learning. In this paper, to obtain readable knowledge from data, we propose a new neuro-fuzzy model and its learning algorithm that works in a parameter space with reduced dimensionality. The dimensionality of the new parameter space is necessary and sufficient to generate human-understandable fuzzy rules, in the sense formally defined by a set of properties. The learning procedure is based on a gradient descent technique and the proposed model is general enough to be applied to other neuro-fuzzy architectures. Simulation studies on a benchmark and a real-life problem are carried out to embody the idea of the paper.