会议专题

Genetic Algorithms for Optimal Fuzzy-Connective Based Aggregation Networks

Multilayer fuzzy connective-based hierarchical aggregation networks simulate the decision-making processes performed by humans, and the results can be interpreted as a set of rules. Identifying the relative importance of the inputs helps to identify redundancies that do not contribute to the decisionmaking process. However, a gradient-based learning approach tends to generate local solutions, and requires the aggregation function to be continuous and differentiable. This study proposes a GA-based learning approach to identify the connective parameters, exploiting the global exploration ability of GAs to improve the quality of solutions. This approach does not require gradient information, making it applicable to both differentiable and nondifferentiable aggregation functions. Statistical analysis of the experimental results confirms that the proposed approach outperforms the gradient-based learning approach, generating more accurate estimates for both generalized mean and gamma operators.

Fang-Fang Wang Chao-Ton Su

Dept.of Industrial Engineering and Engineering Management National Tsing Hua University Hsinchu, Tai Dept.of Industrial Engineering and Engineering Management National Tsing Hua University Hsinchu, Ta

国际会议

International Conference on Management and Service Science(2011年第五届管理与服务科学国际会议 MASS 2011)

武汉

英文

1-4

2011-08-12(万方平台首次上网日期,不代表论文的发表时间)