Một phương pháp tiến hóa đa mục tiêu sinh hệ luật mờ Mamdani với từ ngôn ngữ ngữ nghĩa định tính cho bài toán hồi quy
Abstract
In this paper, we propose a multi-objective evolutionary algorithm, which allows learning concurrently fuzzy rules and linguistic terms along with their fuzzy set based semantics to design Mamdani fuzzy rule-based system (MFRBS) for solving regression problems. The evolutionary algorithm is developed on the schema evolution (2+2)M-PAES in [7,9]. The new method for coding individuals that can only be realized in the hedge algebra (HA) approach to solving this problem and utilizing HA to design linguistic terms intergrated with their semantic. The computer simulation is carried out with 9 standard real regression problems in [10] accepted by the research community and the obtained results show that the MFRBSs are better than those examined in [5] with respect to two objectives: the complexity and the accuracy.References
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