Fuzzy Interpolative Reasoning Using Interval Type-2 Fuzzy Sets
IEA/AIE '08 Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence
Finding input sub-spaces for polymorphic fuzzy signatures
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Temperature prediction based on fuzzy clustering and fuzzy rules interpolation techniques
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Intelligent automated guided vehicle with reverse strategy: a comparison study
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Multi-variable fuzzy forecasting based on fuzzy clustering and fuzzy rule interpolation techniques
Information Sciences: an International Journal
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Weighted fuzzy interpolative reasoning for sparse fuzzy rule-based systems
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Fuzzy rule interpolation based on the ratio of fuzziness of interval type-2 fuzzy sets
Expert Systems with Applications: An International Journal
Fuzzy transforms method in prediction data analysis
Fuzzy Sets and Systems
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Proceedings of the 2012 Joint International Conference on Human-Centered Computer Environments
Signatures: Definitions, operators and applications to fuzzy modelling
Fuzzy Sets and Systems
Fuzzy interpolative reasoning for sparse fuzzy rule-based systems based on the slopes of fuzzy sets
Expert Systems with Applications: An International Journal
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Fuzzy rule based systems have been very popular in many engineering applications. However, when generating fuzzy rules from the available information, this may result in a sparse fuzzy rule base. Fuzzy rule interpolation techniques have been established to solve the problems encountered in processing sparse fuzzy rule bases. In most engineering applications, the use of more than one input variable is common, however, the majority of the fuzzy rule interpolation techniques only present detailed analysis to one input variable case. This paper investigates characteristics of two selected fuzzy rule interpolation techniques for multidimensional input spaces and proposes an improved fuzzy rule interpolation technique to handle multidimensional input spaces. The three methods are compared by means of application examples in the field of petroleum engineering and mineral processing. The results show that the proposed fuzzy rule interpolation technique for multidimensional input spaces can be used in engineering applications.