Proceedings of the 2016 UK Workshop on Computational Intelligence (UKCI), 2016

Best Paper Award Citation: 27^{#}

The Mamdani and TSK fuzzy models are fuzzy inference engines which have been most widely applied in real-world problems. Compared to the Mamdani approach, the TSK approach is more convenient when the crisp outputs are required. Common to both approaches, when a given observation does not overlap with any rule antecedent in the rule base (which usually termed as a sparse rule base), no rule can be fired, and thus no result can be generated. Fuzzy rule interpolation was proposed to address such issue. Although a number of important fuzzy rule interpolation approaches have been proposed in the literature, all of them were developed for Mamdani inference approach, which leads to the fuzzy outputs. This paper extends the traditional TSK fuzzy inference approach to allow inferences on sparse TSK fuzzy rule bases with crisp outputs directly generated. This extension firstly calculates the similarity degrees between a given observation and every individual rule in the rule base, such that the similarity degrees between the observation and all rule antecedents are greater than 0 even when they do not overlap. Then the TSK fuzzy model is extended using the generated matching degrees to derive crisp inference results. The experimentation shows the promising of the approach in enhancing the TSK inference engine when the knowledge represented in the rule base is not complete.

Yao Tan, Jie Li, Martin Wonders, Fei Chao, Hubert P. H. Shum and Longzhi Yang, "Towards Sparse Rule Base Generation for Fuzzy Rule Interpolation", *Proceedings of the 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)*, 2016

Yao Tan, Hubert P. H. Shum, Fei Chao, V. Vijayakumar and Longzhi Yang, "Curvature-Based Sparse Rule Base Generation for Fuzzy Rule Interpolation", *Journal of Intelligent and Fuzzy Systems (JIFS)*, 2019

Jie Li, Hubert P. H. Shum, Xin Fu, Graham Sexton and Longzhi Yang, "Experience-Based Rule Base Generation and Adaptation for Fuzzy Interpolation", *Proceedings of the 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)*, 2016