Published
01/01/2004
Keywords
- Outlier,
- LSE,
- redundancy,
- fuzzy sets
Abstract
ions requires an assessment of the observed values regarding possible outliers or blundners. This is traditionally done using an iterative procedure. In each step, the observations are mutually considered as blundered. The respective error is modelled, estimated and tested for significance estimated error in the respective step is eliminated. The procedure sops when there is no significance. The widespread use of this strategy benefits from its theoretical background, its algorithmic efficiency and its adequacy in standard data processing. However, its main diadvantage is the neglection of multiple blunders in each step. In this study fuzzy logic is used to simultaneously model, estimate and statistically test all possible errors in the observations to overcome this problem. This strategy extending the traditional way is developed and discussed with respect to the classical case. The presentation concludes with numerical examples.