Abstract
In this paper, appropriate least-squares methods were developed to operate in data fusion scenarios. These methods generate optimal estimates by combining measurements from a finite collection of samples. The aggregation operators of the average type, namely, ordered weighted averaging (OWA), Choquet integral, and mixture operators, were applied to formulate the optimization problem. Numerical examples about fitting curves to a given set of points are provided to show the effectiveness of the proposed algorithms.
Subject
Geometry and Topology,Logic,Mathematical Physics,Algebra and Number Theory,Analysis
Reference27 articles.
1. OWA-weighted based clustering method for classification problem;Cheng;Expert Syst. Appl.,2009
2. Aggregation functions on n-dimensional ordered vectors equipped with an admissible order and an application in multi-criteria group decision-making;Milfont;Int. J. Approx. Reason.,2021
3. Forecasting the exchange rate with multiple linear regression and heavy ordered weighted average operators;Flores-Sosa;Eur. J. Oper. Res.,2022
4. Robust Estimation for Uncertain Models in a Data Fusion Scenario;Sayed;IFAC Proc. Vol.,2000
5. Kailath, T., Sayed, A.S., and Hassibi, B. (2000). Linear Estimation, Prentice Hall. [3rd ed.].