Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Granular Computing: An Emerging Paradigm
Granular Computing: An Emerging Paradigm
On the representation of fuzzy rules in terms of crisp rules
Information Sciences—Informatics and Computer Science: An International Journal
Mining association rules from imprecise ordinal data
Fuzzy Sets and Systems
Learning algorithms for a class of neurofuzzy network and application
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 neuro-fuzzy modeling framework with application tomaterial property prediction
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy logic = computing with words
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Neural Networks
Compensatory neurofuzzy systems with fast learning algorithms
IEEE Transactions on Neural Networks
Efficient learning algorithms for three-layer regular feedforward fuzzy neural networks
IEEE Transactions on Neural Networks
On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm
IEEE Transactions on Neural Networks
Advances in Engineering Software
Formal concept analysis based on fuzzy granularity base for different granulations
Fuzzy Sets and Systems
Granular computing neural-fuzzy modelling: A neutrosophic approach
Applied Soft Computing
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Fuzzy and neural-fuzzy systems have successfully and extensively applied to solve problems in many research areas such as those associated with industrial, medical and academic applications. However, recent trends reveal a demand for a workflow with a particular emphasis on transparency, simplicity, system interpretability as well as on a strong link with human cognition. Such requirement is mainly driven by research areas where expert knowledge is of very high importance and any new proposed modelling system falls under the interpretability scrutiny of experts in order to confirm the system's validity. The relatively recent paradigm of granular computing (GrC) offers an ideal opportunity for a transparent knowledge discovery methodology to be combined with fuzzy logic thereby towards a systematic modelling framework with a focus on the overall transparency of the system. Such transparency in the workflow allows for better interaction between the expert process knowledge and the system design which translates into a better performing system. In this paper a systematic modelling approach using granular computing (GrC) and neural-fuzzy modelling is presented. In this research study a GrC algorithm is used to extract relational information and data characteristics out of an initial database. The extracted knowledge and granular features are then translated into a linguistic rule-base of a fuzzy system. This rule-base is finally elicited and optimised via a neural-fuzzy modelling structure. During the various steps of this methodology the transparency features are highlighted and it is shown here how the system designer can take advantage of such features to enhance the system. The proposed modelling framework is applied to a multi-dimensional and complex data set consisting of measurements of mechanical properties of heat treated steel. The data set is collected from a real industrial process and the measurements are dictated by customer production demands and the data set is very sparse with many discontinuities. The proposed framework successfully models the mechanical properties of heat treated steel and it further improves upon the performance of previously established modelling structures.