Amjad AlSakarneh
[1] S. Zhou & J. Gan, Low-level interpretability and high-levelinterpretability: A unified view of data-driven interpretablefuzzy system modelling, Fuzzy Sets and Systems, 159, 2008,3091–3131. [2] W. Ngenkaew, S. Ono, & S. Nakayama, Ant-based clusteringwith multiple deposited pheromones and simple ant memory,Proc. 10th IASTED Int. Conf. Control and Intelligent Systems,2007, 252–256. [3] E. Nasibov & G. Ulutagay, A new unsupervised approach forfuzzy clustering, Fuzzy Sets and Systems, 158, 2007, 2118–2133. [4] G. Tsekourasa, H. Sarimveisb, E. Kavaklia, & G. Bafasb, Ahierarchical fuzzy-clustering approach to fuzzy modeling, FuzzySets and Systems, 150, 2005, 245–266. [5] J. Tovar & W. Yu, Fuzzy neural modeling via clustering andsupport vector machines, 16th IEEE Int. Conf. Control Appl.,Singapore, 2007, 25–29. [6] F. Wana, H. Shangb, L. Wangc, & Y. Suna, How to determinethe minimum number of fuzzy rules to achieve given accuracy:A computational geometric approach to SISO case, Fuzzy Setsand Systems, 150, 2005, 199–209. [7] W. Tjhi & L Chen, A heuristic-based fuzzy co-clusteringalgorithm for categorization of high-dimensional data, FuzzySets and Systems, 159, 2008, 371–389. [8] J. Liua & M. Xub, Kernelized fuzzy attribute C-means cluster-ing algorithm, Fuzzy Sets and Systems, 159, 2008, 2428–2445. [9] H.L. Shieh, C.N. Lee, & Y.K. Yang, A weight-featured anddata-distribution-based fuzzy pattern classification approach,International Journal of Control and Intelligent Systems, 35(4),2007, 300–308. [10] C. Lia & K. Chengb, Recurrent neuro-fuzzy hybrid-learningapproach to accurate system modeling, Fuzzy Sets and Systems,158, 2007, 194–212. [11] P. Phan & T. Gale, Direct adaptive fuzzy control with aself-structuring algorithm, Fuzzy Sets and Systems, 159, 2008,871–899. [12] Z.K. Xue & S.Y. Li, Multi-model modeling and predictivecontrol based on local model networks, International Journalof Control and Intelligent Systems, 34(2), 2006, 105–112. [13] H. Wang, S. Kwong, Y. Jin, W. Wei, & K.F. Man, Multi-objective hierarchical genetic algorithm for interpretable fuzzyrule-based knowledge extraction, Fuzzy Sets and Systems, 149,2005, 149–186.
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