Showing papers by "Dong-Ling Xu published in 2015"

Circuit Tolerance Design Using Belief Rule Base

Abstract: A belief rule-based (BRB) system provides a generic nonlinear modeling and inference mechanism. It is capable of modeling complex causal relationships by utilizing both quantitative information and qualitative knowledge. In this paper, a BRB system is firstly developed to model the highly nonlinear relationship between circuit component parameters and the performance of the circuit by utilizing available knowledge from circuit simulations and circuit designers. By using rule inference in the BRB system and clustering analysis, the acceptability regions of the component parameters can be separated from the value domains of the component parameters. Using the established nonlinear relationship represented by the BRB system, an optimization method is then proposed to seek the optimal feasibility region in the acceptability regions so that the volume of the tolerance region of the component parameters can be maximized. The effectiveness of the proposed methodology is demonstrated through two typical numerical examples of the nonlinear performance functions with nonconvex and disconnected acceptability regions and high-dimensional input parameters and a real-world application in the parameter design of a track circuit for Chinese high-speed railway.

Combined medical quality assessment using the evidential reasoning approach

Abstract: A multiple criteria medical quality assessment framework is proposed.The evidential reasoning approach is applied to combined medical quality assessment.A method for converting numerical indicators to qualitative grades is proposed.A case study of the quality assessment framework and methodology is presented. Due to increasing demand for healthcare, medical quality has attracted significant attention in recent years. Most studies to date have tried to assess medical quality from objective quality indicators or subjective expert judgments or patient feedback perspective. In this study, the evidential reasoning approach is employed to combine objective quality indicators, subjective expert judgments and patient feedback in a multiple criteria framework to assess the quality of hospitals systematically and comprehensively. The evidential reasoning approach has the advantages of consistently handling both subjective evaluations and objective indicators under uncertainty within the same framework, and it can help to provide a robust alternative ranking. This study contributes to the literature with not only a novel medical quality assessment and aggregation framework, but also a pragmatic data transformation technique which can facilitate the combination of quantitative data and qualitative judgments using the evidential reasoning approach. A case study of three top-ranked teaching hospitals in Beijing is presented to demonstrate the framework and methodology proposed in this study.

Identification of uncertain nonlinear systems: constructing belief rule-based models

Abstract: The objective of this paper is to construct reliable belief rule-based (BRB) models for the identification of uncertain nonlinear systems. The BRB methodology is developed from the evidential reasoning (ER) approach and traditional IF–THEN rule based system. It can be used to model complicated nonlinear causal relationships between antecedent attributes and consequents under different types of uncertainty. In a BRB model, various types of information and knowledge with uncertainties can be represented using belief structures, and a belief rule is designed with belief degrees embedded in its possible consequents. In this paper, we first introduce the BRB methodology for modelling uncertain nonlinear systems. Then we present a comparative analysis of three BRB identification models through combining the BRB methodology with nonlinear optimisation techniques. The novel BRB identification models using l8-norm and minimising mean uncertainties in belief rules (MUBR) show remarkable capabilities of capturing the lower and upper bounds of the interval outputs of uncertain nonlinear systems simultaneously. Trade-off analysis between identification accuracy and interval credibility are briefly discussed. Finally, a numerical study of a simplified car dynamics is conducted to demonstrate the capability and effectiveness of the BRB identification models for the modelling and identification of uncertain nonlinear systems.

Research project evaluation and selection: an evidential reasoning rule-based method for aggregating peer review information with reliabilities

Abstract: Research project evaluation and selection is mainly concerned with evaluating a number of research projects and then choosing some of them for implementation. It involves a complex multiple-experts multiple-criteria decision making process. Thus this paper presents an effective method for evaluating and selecting research projects by using the recently-developed evidential reasoning (ER) rule. The proposed ER rule based evaluation and selection method mainly includes (1) using belief structures to represent peer review information provided by multiple experts, (2) employing a confusion matrix for generating experts' reliabilities, (3) implementing utility based information transformation to handle qualitative evaluation criteria with different evaluation grades, and (4) aggregating multiple experts' evaluation information on multiple criteria using the ER rule. An experimental study on the evaluation and selection of research proposals submitted to the National Science Foundation of China demonstrates the applicability and effectiveness of the proposed method. The results show that (1) the ER rule based method can provide consistent and informative support to make informed decisions, and (2) the reliabilities of the review information provided by different experts should be taken into account in a rational research project evaluation and selection process, as they have a significant influence to the selection of eligible projects for panel review.

A Bibliometric Overview of Financial Studies

Abstract: Academic research in modern finance has been developing over the last decades. Many important contributions have been published in the main journals of the field. This paper analyzes scholarly research in finance by using bibliometric indicators. The main results are summarized in three fundamental issues. First, the citation structure in finance is presented. Next, the paper studies the influence of financial journals by using a wide range of indicators including publications, citations and the h-index. The paper ends with an overview of the most influential papers. In general, the results are in accordance with the expectations where the Journal of Finance, the Journal of Financial Economics and the Review of Financial Studies are the most popular journals and the USA is clearly the dominant country in finance.