In existing linguistic decision-making(LDM)methods,individual decision maker generally evaluates alternatives through a single-round evaluation process,in which only preliminary cognition of individual decision maker ...In existing linguistic decision-making(LDM)methods,individual decision maker generally evaluates alternatives through a single-round evaluation process,in which only preliminary cognition of individual decision maker can be excavated.This results in the evaluation provided by individual decision maker may not well reflect their integrated preference through the single-round evaluation process.To address this issue,a multi-round evaluation process should be designed,in which the decision maker can constantly renew his or her acquired cognition through previous rounds of evaluation and further updates his or her evaluation.In this paper,a cognitive-driven LDM method based on the multi-round evaluation process is proposed to overcome the insufficiency of existing methods in adequately exploring the decision maker comprehensive cognition.First,the transition process of linguistic term(LT)of the alternative induced by decision maker’s cognition renewal is modeled as a Markov chain.The transition probability from one state to another within the state space is then created to obtain the incomplete transition matrix,whose entropy rate is maximized to derive the complete transition matrix via the constructed convex optimization problem.The stable distributions of alternatives can be generated based on their complete transition matrices.The aggregated stable distributions of alternatives are obtained by minimizing the dissimilarity between them and the individual stable distributions of alternatives on the criteria.On this basis,the ranking order of alternatives can be generated.The proposed method is further applied to a diagnostic ultrasound system selection problem for demonstrating its applicability and effectiveness.The comparative experiment reveals the significance of considering decision maker’s renewal cognition in the multi-round decision-making process.This paper provides insights on improving decision-making quality through the modeling of decision maker’s acquired cognition in the multi-round decision-making processes.展开更多
Dynamic changes of traffic features in unstructured road networks challenge the scene-cognitive abilities of drivers,which brings various heterogeneous traffic behaviors.Modeling traffic with these heterogeneous behav...Dynamic changes of traffic features in unstructured road networks challenge the scene-cognitive abilities of drivers,which brings various heterogeneous traffic behaviors.Modeling traffic with these heterogeneous behaviors would have significant impact on realistic traffic simulation.Most existing traffic methods generate traffic behaviors by adjust-ing parameters and cannot describe those heterogeneous traffic flows in detail.In this paper,a cognition-driven traffic-simulation method inspired by the theory of cognitive psychology is introduced.We first present a visual-filtering model and a perceptual-information fusion model to describe drivers'heterogeneous cognitive processes.Then,logistic regression is used to model drivers'heuristic decision-making processes based on the above cognitive results.Lastly,we apply the high-level cognitive decision-making results to low-level traffic simulation.The experimental results show that our method can provide realistic simulations for the traffic with those heterogeneous behaviors in unstructured road networks and has nearly the same efficiency as that of existing methods.展开更多
基金supported by the National Natural Science Foundation of China(Nos.72101074 and 72201089)Fundamental Research Funds for the Central Universities(No.JZ2023HGTB0275).
文摘In existing linguistic decision-making(LDM)methods,individual decision maker generally evaluates alternatives through a single-round evaluation process,in which only preliminary cognition of individual decision maker can be excavated.This results in the evaluation provided by individual decision maker may not well reflect their integrated preference through the single-round evaluation process.To address this issue,a multi-round evaluation process should be designed,in which the decision maker can constantly renew his or her acquired cognition through previous rounds of evaluation and further updates his or her evaluation.In this paper,a cognitive-driven LDM method based on the multi-round evaluation process is proposed to overcome the insufficiency of existing methods in adequately exploring the decision maker comprehensive cognition.First,the transition process of linguistic term(LT)of the alternative induced by decision maker’s cognition renewal is modeled as a Markov chain.The transition probability from one state to another within the state space is then created to obtain the incomplete transition matrix,whose entropy rate is maximized to derive the complete transition matrix via the constructed convex optimization problem.The stable distributions of alternatives can be generated based on their complete transition matrices.The aggregated stable distributions of alternatives are obtained by minimizing the dissimilarity between them and the individual stable distributions of alternatives on the criteria.On this basis,the ranking order of alternatives can be generated.The proposed method is further applied to a diagnostic ultrasound system selection problem for demonstrating its applicability and effectiveness.The comparative experiment reveals the significance of considering decision maker’s renewal cognition in the multi-round decision-making process.This paper provides insights on improving decision-making quality through the modeling of decision maker’s acquired cognition in the multi-round decision-making processes.
基金supported by the National Natural Science Foundation of China under Grant Nos.61602425,61572445 and 61822701the Doctor Fund of Zhengzhou University of Light Industry of China under Grant No.2015BSJJ007。
文摘Dynamic changes of traffic features in unstructured road networks challenge the scene-cognitive abilities of drivers,which brings various heterogeneous traffic behaviors.Modeling traffic with these heterogeneous behaviors would have significant impact on realistic traffic simulation.Most existing traffic methods generate traffic behaviors by adjust-ing parameters and cannot describe those heterogeneous traffic flows in detail.In this paper,a cognition-driven traffic-simulation method inspired by the theory of cognitive psychology is introduced.We first present a visual-filtering model and a perceptual-information fusion model to describe drivers'heterogeneous cognitive processes.Then,logistic regression is used to model drivers'heuristic decision-making processes based on the above cognitive results.Lastly,we apply the high-level cognitive decision-making results to low-level traffic simulation.The experimental results show that our method can provide realistic simulations for the traffic with those heterogeneous behaviors in unstructured road networks and has nearly the same efficiency as that of existing methods.
