Elliptical distribution-based weight-determining method for ordered weighted averaging operators

Abstract

The ordered weighted averaging (OWA) operators play a crucial role in aggregating multiple criteria evaluations into an overall assessment supporting the decision makers’ choice. One key point steps is to determine the associated weights. In this paper, we first briefly review some main methods for determining the weights by using distribution functions. Then we propose a new approach for determining OWA weights by using the regular increasing monotone quantifier. Motivated by the idea of normal distribution‐based method to determine the OWA weights, we develop a method based on elliptical distributions for determining the OWA weights, and some of its desirable properties have been investigated.

State estimation for bilinear systems through minimizing the covariance matrix of the state estimation errors

“…The methods proposed in this paper can combine some statistical optimal strategies [44][45][46][47] to study the parameter estimation algorithms of linear and nonlinear systems [48][49][50][51][52] and can be applied to other fields, [53][54][55][56][57][58][59] such as fault detection, image processing, and sliding mode control. Different from the previous linearization method like Taylor expansion, we take use of the special structure of the bilinear system and propose the state filtering algorithm to obtain the unknown states by minimizing the covariance matrix of the state estimation errors based on the extremum principle.…”

“…Finally, the convergence analysis and the simulation results show that the proposed state estimator has good performance in the state estimation of bilinear systems. The methods proposed in this paper can combine some statistical optimal strategies [44][45][46][47] to study the parameter estimation algorithms of linear and nonlinear systems [48][49][50][51][52] and can be applied to other fields, [53][54][55][56][57][58][59] such as fault detection, image processing, and sliding mode control. [60][61][62][63]…”

Maximum likelihood gradient identification for multivariate equation‐error moving average systems using the multi‐innovation theory

“…The methods proposed in this paper can combine other mathematical tools [52][53][54] to study the parameter identification problems of different systems with colored noise [55][56][57] and can be applied to other fields such as information processing and communication. [58][59][60][61][62][63][64][65][66][67]…”

“…principle is an effective method to improve the accuracy of the identification algorithms. [58][59][60][61][62][63][64][65][66][67] [58][59][60][61][62][63][64][65][66][67] …”

Decomposition Least-Squares-Based Iterative Identification Algorithms for Multivariable Equation-Error Autoregressive Moving Average Systems

“…Compared with the LSI algorithm, the HLSI algorithm can obtain more accurate parameter estimates and higher computational efficiency for multivariable systems with the colored noise. Furthermore, the proposed approaches in the paper can combine other mathematical optimization approaches [43][44][45][46][47] and statistical strategies [48][49][50][51][52][53][54] to study the parameter estimation problems of linear and nonlinear systems with different disturbances [55][56][57][58][59][60], and can be applied to other fields [61][62][63][64] such as signal processing [65][66][67][68][69][70].…”