Title :
Genetic Algorithms for Optimal Fuzzy-Connective-Based Aggregation Networks
Author :
Wang, Fang-Fang ; Su, Chao-Ton
Author_Institution :
Dept. of Ind. Eng. & Eng. Manage., Nat. Tsing Hua Univ., Hsinchu, Taiwan
Abstract :
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 decision-making 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.
Keywords :
decision making; fuzzy neural nets; genetic algorithms; gradient methods; learning (artificial intelligence); multilayer perceptrons; statistical analysis; decision making process; gamma operators; genetic algorithm based learning approach; global exploration; gradient based learning approach; gradient information; multilayer fuzzy connective based hierarchical aggregation network; nondifferentiable aggregation functions; optimal fuzzy connective based aggregation network; statistical analysis; Accuracy; Analysis of variance; Biological cells; Decision making; Fuzzy sets; Genetic algorithms; Nonhomogeneous media;
Conference_Titel :
Management and Service Science (MASS), 2011 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-6579-8
DOI :
10.1109/ICMSS.2011.5999353