On the use of fuzzy inference techniques in assessment models: part I--theoretical properties
Fuzzy Optimization and Decision Making
Towards adaptive interpolative reasoning
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Preserving piece-wise linearity in fuzzy interpolation
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
Multi-variable fuzzy forecasting based on fuzzy clustering and fuzzy rule interpolation techniques
Information Sciences: an International Journal
IEEE Transactions on Fuzzy Systems
Weighted fuzzy interpolative reasoning for sparse fuzzy rule-based systems
Expert Systems with Applications: An International Journal
A fuzzy inference system-based criterion-referenced assessment model
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
An affine fuzzy model with local and global interpretations
Applied Soft Computing
Fuzzy rule interpolation based on the ratio of fuzziness of interval type-2 fuzzy sets
Expert Systems with Applications: An International Journal
Fuzzy interpolative reasoning for sparse fuzzy rule-based systems based on the slopes of fuzzy sets
Expert Systems with Applications: An International Journal
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Hybrid approaches for approximate reasoning
| Hi-index | 0.01 |
Fuzzy interpolation does not only help to reduce the complexity of fuzzy models, but also makes inference in sparse rule-based systems possible. It has been successfully applied to systems control, but limited work exists for its applications to tasks like prediction and classification. Almost all fuzzy interpolation techniques in the literature make strong assumptions that there are two closest adjacent rules available to the observation, and that such rules must flank the observation for each attribute. Also, some interpolation approaches cannot handle fuzzy sets whose membership functions involve vertical slopes. To avoid such limitations and develop a more practical approach, this paper extends the work of Huang and Shen. The result enables both interpolation and extrapolation which involve multiple fuzzy rules, with each rule consisting of multiple antecedents. Two realistic applications, namely truck backer-upper control and computer activity prediction, are provided in this paper to demonstrate the utility of the extended approach. Experiment-based comparisons to the most commonly used Mamdani fuzzy reasoning mechanism, and to other existing fuzzy interpolation techniques are given to show the significance and potential of this research.