Name
Abstract | ||
|---|---|---|
As a classical aggregation tool, the weighted average method is widely used in information fusion. It is the Lebesgue integral with respect to the weights essentially. Due to some inherent interaction among diverse information sources, the weighted average method does not work well in many real problems. To describe the interaction, an intuitive and effective way is to replace the additive weights with a nonadditive set function defined on the power set of the set of all information sources. Instead of the weighted average method, we should use the Choquet integral or some other nonlinear integrals, especially, the new nonlinear integral introduced by the authors recently. The crux of making such an improvement is how to determine the nonadditive set function from given input-output data when the nonlinear integral is viewed as a multi-input single-output system. In this paper, we employ a specially designed genetic algorithm to realize the optimization in determining the nonadditive set function. |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1016/S0165-0114(98)00220-6 | Fuzzy Sets and Systems |
Keywords | Field | DocType |
nonadditive set functions,nonlinear integrals,information fusion,optimization,genetic algorithms | Set function,Discrete mathematics,Mathematical optimization,Information processing,Nonlinear system,Measure (mathematics),Choquet integral,Power set,Mathematics,Lebesgue integration,Genetic algorithm | Journal |
Volume | Issue | ISSN |
102 | 3 | Fuzzy Sets and Systems |
Citations | PageRank | References |
44 | 3.24 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhenyuan Wang | 1 | 684 | 90.22 |
| Kwong-Sak Leung | 2 | 1887 | 205.58 |
| Jia Wang | 3 | 44 | 3.24 |