High complexity and uncertainty of air combat pose significant challenges to target intention prediction.Current interpolation methods for data pre-processing and wrangling have limitations in capturing interrelations...High complexity and uncertainty of air combat pose significant challenges to target intention prediction.Current interpolation methods for data pre-processing and wrangling have limitations in capturing interrelationships among intricate variable patterns.Accordingly,this study proposes a Mogrifier gate recurrent unit-D(Mog-GRU-D)model to address the com-bat target intention prediction issue under the incomplete infor-mation condition.The proposed model directly processes miss-ing data while reducing the independence between inputs and output states.A total of 1200 samples from twelve continuous moments are captured through the combat simulation system,each of which consists of seven dimensional features.To bench-mark the experiment,a missing valued dataset has been gener-ated by randomly removing 20%of the original data.Extensive experiments demonstrate that the proposed model obtains the state-of-the-art performance with an accuracy of 73.25%when dealing with incomplete information.This study provides possi-ble interpretations for the principle of target interactive mecha-nism,highlighting the model’s effectiveness in potential air war-fare implementation.展开更多
In this paper,a distributed adaptive dynamic programming(ADP)framework based on value iteration is proposed for multi-player differential games.In the game setting,players have no access to the information of others&#...In this paper,a distributed adaptive dynamic programming(ADP)framework based on value iteration is proposed for multi-player differential games.In the game setting,players have no access to the information of others'system parameters or control laws.Each player adopts an on-policy value iteration algorithm as the basic learning framework.To deal with the incomplete information structure,players collect a period of system trajectory data to compensate for the lack of information.The policy updating step is implemented by a nonlinear optimization problem aiming to search for the proximal admissible policy.Theoretical analysis shows that by adopting proximal policy searching rules,the approximated policies can converge to a neighborhood of equilibrium policies.The efficacy of our method is illustrated by three examples,which also demonstrate that the proposed method can accelerate the learning process compared with the centralized learning framework.展开更多
This paper presents a novel cooperative value iteration(VI)-based adaptive dynamic programming method for multi-player differential game models with a convergence proof.The players are divided into two groups in the l...This paper presents a novel cooperative value iteration(VI)-based adaptive dynamic programming method for multi-player differential game models with a convergence proof.The players are divided into two groups in the learning process and adapt their policies sequentially.Our method removes the dependence of admissible initial policies,which is one of the main drawbacks of the PI-based frameworks.Furthermore,this algorithm enables the players to adapt their control policies without full knowledge of others’ system parameters or control laws.The efficacy of our method is illustrated by three examples.展开更多
This paper conducts a survey on iterative learn-ing control(ILC)with incomplete information and associated control system design,which is a frontier of the ILC field.The incomplete information,including passive and ac...This paper conducts a survey on iterative learn-ing control(ILC)with incomplete information and associated control system design,which is a frontier of the ILC field.The incomplete information,including passive and active types,can cause data loss or fragment due to various factors.Passive incomplete information refers to incomplete data and information caused by practical system limitations during data collection,storage,transmission,and processing,such as data dropouts,delays,disordering,and limited transmission bandwidth.Active incomplete information refers to incomplete data and information caused by man-made reduction of data quantity and quality on the premise that the given objective is satisfied,such as sampling and quantization.This survey emphasizes two aspects:the first one is how to guarantee good learning performance and tracking performance with passive incomplete data,and the second is how to balance the control performance index and data demand by active means.The promising research directions along this topic are also addressed,where data robustness is highly emphasized.This survey is expected to improve understanding of the restrictive relationship and trade-off between incomplete data and tracking performance,quantitatively,and promote further developments of ILC theory.展开更多
This study analyzes the cooperative coalition problem for formation scheduling based on incomplete information. A multi-agent cooperative coalition framework is developed to optimize the formation scheduling problem i...This study analyzes the cooperative coalition problem for formation scheduling based on incomplete information. A multi-agent cooperative coalition framework is developed to optimize the formation scheduling problem in a decentralized manner. The social class differentiation mech- anism and role-assuming mechanism are incorporated into the framework, which, in turn, ensures that the multi-agent system （MAS） evolves in the optimal direction. Moreover, a further differen- tiation pressure can be achieved to help MAS escape from local optima. A Bayesian coalition nego- tiation algorithm is constructed, within which the Harsanyi transformation is introduced to transform the coalition problem based on incomplete information to the Bayesian-equivalent coali- tion problem based on imperfect information. The simulation results suggest that the distribution of agents＇ expectations of other agents＇ unknown information approximates to the true distribution after a finite set of generations. The comparisons indicate that the MAS cooperative coalition algo- rithm produces a significantly better utility and possesses a more effective capability of escaping from local optima than the proposal-engaged marriage algorithm and the Simulated Annealing algorithm.展开更多
Cooperative autonomous air combat of multiple unmanned aerial vehicles(UAVs)is one of the main combat modes in future air warfare,which becomes even more complicated with highly changeable situation and uncertain info...Cooperative autonomous air combat of multiple unmanned aerial vehicles(UAVs)is one of the main combat modes in future air warfare,which becomes even more complicated with highly changeable situation and uncertain information of the opponents.As such,this paper presents a cooperative decision-making method based on incomplete information dynamic game to generate maneuver strategies for multiple UAVs in air combat.Firstly,a cooperative situation assessment model is presented to measure the overall combat situation.Secondly,an incomplete information dynamic game model is proposed to model the dynamic process of air combat,and a dynamic Bayesian network is designed to infer the tactical intention of the opponent.Then a reinforcement learning framework based on multiagent deep deterministic policy gradient is established to obtain the perfect Bayes-Nash equilibrium solution of the air combat game model.Finally,a series of simulations are conducted to verify the effectiveness of the proposed method,and the simulation results show effective synergies and cooperative tactics.展开更多
The threat sequencing of multiple unmanned combat air vehicles(UCAVs) is a multi-attribute decision-making(MADM)problem. In the threat sequencing process of multiple UCAVs,due to the strong confrontation and high dyna...The threat sequencing of multiple unmanned combat air vehicles(UCAVs) is a multi-attribute decision-making(MADM)problem. In the threat sequencing process of multiple UCAVs,due to the strong confrontation and high dynamics of the air combat environment, the weight coefficients of the threat indicators are usually time-varying. Moreover, the air combat data is difficult to be obtained accurately. In this study, a threat sequencing method of multiple UCAVs is proposed based on game theory by considering the incomplete information. Firstly, a zero-sum game model of decision maker( D) and nature(N)with fuzzy payoffs is established to obtain the uncertain parameters which are the weight coefficient parameters of the threat indicators and the interval parameters of the threat matrix. Then,the established zero-sum game with fuzzy payoffs is transformed into a zero-sum game with crisp payoffs(matrix game) to solve. Moreover, a decision rule is addressed for the threat sequencing problem of multiple UCAVs based on the obtained uncertain parameters. Finally, numerical simulation results are presented to show the effectiveness of the proposed approach.展开更多
The different conditions of use of a component result in a variety of damage levels.Therefore,excluding differences in shape and size,used parts show a high degree of uncertainty regarding failure characteristics,qual...The different conditions of use of a component result in a variety of damage levels.Therefore,excluding differences in shape and size,used parts show a high degree of uncertainty regarding failure characteristics,quality conditions,and remaining life,which seriously affects the efficiency of a remanufacturing scheme design.Aiming to address this problem,a remanufacturing scheme design method based on the reconstruction of incomplete information of used parts is proposed.First,the remaining life of the reconstructed model is predicted by finite element analysis,and the demand for the next life cycle is determined.Second,the scanned 3D damage point cloud data are registered with the original point cloud data using the integral iterative method to construct a missing point cloud model to achieve the restoration of geometric information.Then,according to reverse engineering and laser cladding remanufacturing,the tool remanufacturing process path can be generated by the tool contact point path section line method.Finally,the proposed method is adopted for turbine blades to evaluate the effectiveness and feasibility of the proposed scheme.This study proposes a remanufacturing scheme design method based on the incomplete reconstruction of used part information to solve the uncertain and highly personalized problems in remanufacturing.展开更多
The weights of criteria are incompletely known and the criteria values are incomplete and uncertain or even default in some fuzzy multi-criteria decision-making problems.For those problems,an approach based on evident...The weights of criteria are incompletely known and the criteria values are incomplete and uncertain or even default in some fuzzy multi-criteria decision-making problems.For those problems,an approach based on evidential reasoning is proposed,in which the criteria values are integrated on the basis of analytical algorithm of evidential reasoning,and then nonlinear programming models of each alternative are developed with the incomplete information on weights.The genetic algorithm is employed to solve the models,producing the weights and the utility interval of each alternative,and the ranking of the whole set of alternatives can be attained.Finally,an example shows the effectiveness of the method.展开更多
The generalized linear model（GLM） based on the observed data with incomplete information in the case of random censorship is defined. Under the given conditions, the existence and uniqueness of the solution on the l...The generalized linear model（GLM） based on the observed data with incomplete information in the case of random censorship is defined. Under the given conditions, the existence and uniqueness of the solution on the likelihood equations with respect to the parameter vector β of the model are discussed, and the consistency and asymptotic normality of the maximum likelihood estimator（MLE） βn^-, are proved.展开更多
As a core component of the network,web applications have become one of the preferred targets for attackers because the static configuration of web applications simplifies the exploitation of vulnerabilities by attacke...As a core component of the network,web applications have become one of the preferred targets for attackers because the static configuration of web applications simplifies the exploitation of vulnerabilities by attackers.Although the moving target defense(MTD)has been proposed to increase the attack difficulty for the attackers,there is no solo approach can cope with different attacks;in addition,it is impossible to implement all these approaches simultaneously due to the resource limitation.Thus,the selection of an optimal defense strategy based on MTD has become the focus of research.In general,the confrontation of two players in the security domain is viewed as a stochastic game,and the reward matrices are known to both players.However,in a real security confrontation,this scenario represents an incomplete information game.Each player can only observe the actions performed by the opponent,and the observed actions are not completely accurate.To accurately describe the attacker’s reward function to reach the Nash equilibrium,this work simulated and updated the strategy selection distribution of the attacker by observing and investigating the strategy selection history of the attacker.Next,the possible rewards of the attacker in each confrontation via the observation matrix were corrected.On this basis,the Nash-Q learning algorithm with reward quantification was proposed to select the optimal strategy.Moreover,the performances of the Minimax-Q learning algorithm and Naive-Q learning algorithm were compared and analyzed in the MTD environment.Finally,the experimental results showed that the strategy selection algorithm can enable defenders to select a more reasonable defensive strategy and achieve the maximum possible reward.展开更多
Based on the rough set theory which is a powerful tool in dealing with vagueness and uncertainty, an algorithm to mine association rules in incomplete information systems was presented and the support and confidence w...Based on the rough set theory which is a powerful tool in dealing with vagueness and uncertainty, an algorithm to mine association rules in incomplete information systems was presented and the support and confidence were redefined. The algorithm can mine the association rules with decision attributes directly without processing missing values. Using the incomplete dataset Mushroom from UCI machine learning repository, the new algorithm was compared with the classical association rules mining algorithm based on Apriori from the number of rules extracted, testing accuracy and execution time. The experiment results show that the new algorithm has advantages of short execution time and high accuracy.展开更多
Incompleteness of information about objects may be the greatest obstruct to performing induction learning from examples. In this paper, the concept of limited-non-symmetric similarity relation is used to formulate a n...Incompleteness of information about objects may be the greatest obstruct to performing induction learning from examples. In this paper, the concept of limited-non-symmetric similarity relation is used to formulate a new definition of approximation to an incomplete information system. With the new definition of approximation to an object set and the concept of attribute value pair, rough-setsbased methodology for certain rule acquisition in an incomplete information system is developed. The algorithm can deal with incomplete data directly and does not require changing the size of the original incomplete system. Experiments show that the algorithm provides precise and simple certain decision rules and is not affected by the missing values.展开更多
It is helpful for people to understand the essence of rough set theory to study the concepts and operations of rough set theory from its information view. In this paper we address knowledge expression and knowledge re...It is helpful for people to understand the essence of rough set theory to study the concepts and operations of rough set theory from its information view. In this paper we address knowledge expression and knowledge reduction in incomplete infolvnation systems from the information view of rough set theory. First, by extending information entropy-based measures in complete information systems, two new measures of incomplete entropy and incomplete conditional entropy are presented for incomplete information systems. And then, based on these measures the problem of knowledge reduction in incomplete information systems is analyzed and the reduct definitions in incomplete information system and incomplete decision table are proposed respectively. Finally, the reduct definitions based on incomplete entropy and the reduct definitions based on similarity relation are compared. Two equivalent relationships between them are proved by theorems and an in equivalent relationship between them is illustrated by an example. The work of this paper extends the research of rough set theory from information view to incomplete information systems and establishes the theoretical basis for seeking efficient algorithm of knowledge acquisition in incomplete information systems.展开更多
Utilized fundamental theory and analysis method of Incomplete Information repeated games, introduced Incomplete Information into repeated games, and established two stages dynamic games model of the local authority an...Utilized fundamental theory and analysis method of Incomplete Information repeated games, introduced Incomplete Information into repeated games, and established two stages dynamic games model of the local authority and the coal mine owner. The analytic result indicates that： so long as the country established the corresponding rewards and punishments incentive mechanism to the local authority departments responsible for the work, it reports the safety accident in the coal mine on time. The conclusion that the local government displays right and wrong cooperation behavior will be changed with the introduction of the Incomplete Information. Only has the local authority fulfill their responsibility, can the unsafe accident be controlled effectively. Once this kind of cooperation of local government appears, the costs of the country on the safe supervise and the difficulty will be able to decrease greatly.展开更多
Right randomly censored data with incomplete infor-mation are frequently met in practice.Although much study about right randomly censored data has been seen in the proportional hazards model,relatively little is know...Right randomly censored data with incomplete infor-mation are frequently met in practice.Although much study about right randomly censored data has been seen in the proportional hazards model,relatively little is known about the inference of regression parameters for right randomly censored data with in-complete information in such model.In particular,theoretical properties of the maximum likelihood estimator of the regression parameters have not been proven yet in that model.In this paper,we show the consistency and asymptotic normality of the maxi-mum likelihood estimator of unknown regression parameters.展开更多
In this paper, we have discussed a random censoring test with incomplete information, and proved that the maximum likelihood estimator(MLE) of the parameter based on the randomly censored data with incomplete informat...In this paper, we have discussed a random censoring test with incomplete information, and proved that the maximum likelihood estimator(MLE) of the parameter based on the randomly censored data with incomplete information in the case of the exponential distribution has the strong consistency.展开更多
In this paper we address information measures of roughness of knowledge and rough sets for incomplete information systems. The definition of rough entropy of knowledge and its important properties are given. In partic...In this paper we address information measures of roughness of knowledge and rough sets for incomplete information systems. The definition of rough entropy of knowledge and its important properties are given. In particular, the relationship between rough entropy of knowledge and the Hartley measure of uncertainty is established. We show that rough entropy of know1edge decreases monotonously as granularity of information become smaller. This gives an information interpretation for roughness of knowledge. Based on rough entropy of knowledge and roughness of rough set. a definition of rough entropy of rough set is proposed, and we show that rough entropy of rough set decreases monotonousIy as granularity of information become smaller. This gives more accurate measure for roughness of rough set.展开更多
To extract and express the knowledge hidden in information systems, discernibility matrix and its extensions were introduced and applied successfully in many real life applications. Binary discernibility matrix, as a ...To extract and express the knowledge hidden in information systems, discernibility matrix and its extensions were introduced and applied successfully in many real life applications. Binary discernibility matrix, as a representative approach, has many interesting superior properties and has been rapidly developed to find intuitive and easy to understand knowledge. However, at present, the binary discernibility matrix is mainly adopted in the complete information system. It is a challenging topic how to achieve the attribute reduction by using binary discernibility matrix in incomplete information system. A form of generalized binary discernibility matrix is further developed for a number of representative extended rough set models that deal with incomplete information systems. Some useful properties and criteria are introduced for judging the attribute core and attribute relative reduction. Thereafter, a new algorithm is formulated which supports attribute core and attribute relative reduction based on the generalized binary discernibility matrix. This algorithm is not only suitable for consistent information systems but also inconsistent information systems. The feasibility of the proposed methods was demonstrated by worked examples and experimental analysis.展开更多
Since web services intended for the same application environment tend to be functionally homogeneous, researchers have turned to their non-functional aspects in order to constructively compare web services and choose ...Since web services intended for the same application environment tend to be functionally homogeneous, researchers have turned to their non-functional aspects in order to constructively compare web services and choose an appropriate one. In general, quality of service is very important to users. Many schemes that consider quality assessment have been proposed for web service selection. With the increasing number of qualityrelated attributes, an effective quality assessment method needs satisfactory scalability. Schemes based on the analytic hierarchy process(AHP) meet this requirement. However, prevalent methods in this vein overlook the fact that the traditional AHP needs a complete judgment matrix. In practice, all the information needed to construct a judgment matrix is often unavailable for a variety of reasons, due to which certain judgments cannot be made. In this paper, for an incomplete judgment matrix, we propose an improved AHP approach to consistency verification and the subsequent ordering. Our method can deal with situations where information is insufficient,and inherits all the merits of the traditional AHP approach. A case study establishes the effectiveness of our proposed method.展开更多
基金supported by the Aeronautical Science Foundation of China(2020Z023053002).
文摘High complexity and uncertainty of air combat pose significant challenges to target intention prediction.Current interpolation methods for data pre-processing and wrangling have limitations in capturing interrelationships among intricate variable patterns.Accordingly,this study proposes a Mogrifier gate recurrent unit-D(Mog-GRU-D)model to address the com-bat target intention prediction issue under the incomplete infor-mation condition.The proposed model directly processes miss-ing data while reducing the independence between inputs and output states.A total of 1200 samples from twelve continuous moments are captured through the combat simulation system,each of which consists of seven dimensional features.To bench-mark the experiment,a missing valued dataset has been gener-ated by randomly removing 20%of the original data.Extensive experiments demonstrate that the proposed model obtains the state-of-the-art performance with an accuracy of 73.25%when dealing with incomplete information.This study provides possi-ble interpretations for the principle of target interactive mecha-nism,highlighting the model’s effectiveness in potential air war-fare implementation.
基金supported by the Aeronautical Science Foundation of China(20220001057001)an Open Project of the National Key Laboratory of Air-based Information Perception and Fusion(202437)
文摘In this paper,a distributed adaptive dynamic programming(ADP)framework based on value iteration is proposed for multi-player differential games.In the game setting,players have no access to the information of others'system parameters or control laws.Each player adopts an on-policy value iteration algorithm as the basic learning framework.To deal with the incomplete information structure,players collect a period of system trajectory data to compensate for the lack of information.The policy updating step is implemented by a nonlinear optimization problem aiming to search for the proximal admissible policy.Theoretical analysis shows that by adopting proximal policy searching rules,the approximated policies can converge to a neighborhood of equilibrium policies.The efficacy of our method is illustrated by three examples,which also demonstrate that the proposed method can accelerate the learning process compared with the centralized learning framework.
基金supported by the Industry-University-Research Cooperation Fund Project of the Eighth Research Institute of China Aerospace Science and Technology Corporation (USCAST2022-11)Aeronautical Science Foundation of China (20220001057001)。
文摘This paper presents a novel cooperative value iteration(VI)-based adaptive dynamic programming method for multi-player differential game models with a convergence proof.The players are divided into two groups in the learning process and adapt their policies sequentially.Our method removes the dependence of admissible initial policies,which is one of the main drawbacks of the PI-based frameworks.Furthermore,this algorithm enables the players to adapt their control policies without full knowledge of others’ system parameters or control laws.The efficacy of our method is illustrated by three examples.
基金supported by the National Natural Science Foundation of China(61673045)Beijing Natural Science Foundation(4152040)
文摘This paper conducts a survey on iterative learn-ing control(ILC)with incomplete information and associated control system design,which is a frontier of the ILC field.The incomplete information,including passive and active types,can cause data loss or fragment due to various factors.Passive incomplete information refers to incomplete data and information caused by practical system limitations during data collection,storage,transmission,and processing,such as data dropouts,delays,disordering,and limited transmission bandwidth.Active incomplete information refers to incomplete data and information caused by man-made reduction of data quantity and quality on the premise that the given objective is satisfied,such as sampling and quantization.This survey emphasizes two aspects:the first one is how to guarantee good learning performance and tracking performance with passive incomplete data,and the second is how to balance the control performance index and data demand by active means.The promising research directions along this topic are also addressed,where data robustness is highly emphasized.This survey is expected to improve understanding of the restrictive relationship and trade-off between incomplete data and tracking performance,quantitatively,and promote further developments of ILC theory.
基金supported by the National Natural Science Foundation of China(No.61039001)the National Science and Technology Support Program of China(No.2011BAH24B10)
文摘This study analyzes the cooperative coalition problem for formation scheduling based on incomplete information. A multi-agent cooperative coalition framework is developed to optimize the formation scheduling problem in a decentralized manner. The social class differentiation mech- anism and role-assuming mechanism are incorporated into the framework, which, in turn, ensures that the multi-agent system （MAS） evolves in the optimal direction. Moreover, a further differen- tiation pressure can be achieved to help MAS escape from local optima. A Bayesian coalition nego- tiation algorithm is constructed, within which the Harsanyi transformation is introduced to transform the coalition problem based on incomplete information to the Bayesian-equivalent coali- tion problem based on imperfect information. The simulation results suggest that the distribution of agents＇ expectations of other agents＇ unknown information approximates to the true distribution after a finite set of generations. The comparisons indicate that the MAS cooperative coalition algo- rithm produces a significantly better utility and possesses a more effective capability of escaping from local optima than the proposal-engaged marriage algorithm and the Simulated Annealing algorithm.
基金supported by the National Natural Science Foundation of China(Grant No.61933010 and 61903301)Shaanxi Aerospace Flight Vehicle Design Key Laboratory。
文摘Cooperative autonomous air combat of multiple unmanned aerial vehicles(UAVs)is one of the main combat modes in future air warfare,which becomes even more complicated with highly changeable situation and uncertain information of the opponents.As such,this paper presents a cooperative decision-making method based on incomplete information dynamic game to generate maneuver strategies for multiple UAVs in air combat.Firstly,a cooperative situation assessment model is presented to measure the overall combat situation.Secondly,an incomplete information dynamic game model is proposed to model the dynamic process of air combat,and a dynamic Bayesian network is designed to infer the tactical intention of the opponent.Then a reinforcement learning framework based on multiagent deep deterministic policy gradient is established to obtain the perfect Bayes-Nash equilibrium solution of the air combat game model.Finally,a series of simulations are conducted to verify the effectiveness of the proposed method,and the simulation results show effective synergies and cooperative tactics.
基金supported by the Major Projects for Science and Technology Innovation 2030 (2018AAA0100805)。
文摘The threat sequencing of multiple unmanned combat air vehicles(UCAVs) is a multi-attribute decision-making(MADM)problem. In the threat sequencing process of multiple UCAVs,due to the strong confrontation and high dynamics of the air combat environment, the weight coefficients of the threat indicators are usually time-varying. Moreover, the air combat data is difficult to be obtained accurately. In this study, a threat sequencing method of multiple UCAVs is proposed based on game theory by considering the incomplete information. Firstly, a zero-sum game model of decision maker( D) and nature(N)with fuzzy payoffs is established to obtain the uncertain parameters which are the weight coefficient parameters of the threat indicators and the interval parameters of the threat matrix. Then,the established zero-sum game with fuzzy payoffs is transformed into a zero-sum game with crisp payoffs(matrix game) to solve. Moreover, a decision rule is addressed for the threat sequencing problem of multiple UCAVs based on the obtained uncertain parameters. Finally, numerical simulation results are presented to show the effectiveness of the proposed approach.
基金Supported by Plateau Disciplines in ShanghaiNational Natural Science Foundation of China (Grant No. 51675388)Hubei Provincial Department of Education of China (Grant No. D20181102)
文摘The different conditions of use of a component result in a variety of damage levels.Therefore,excluding differences in shape and size,used parts show a high degree of uncertainty regarding failure characteristics,quality conditions,and remaining life,which seriously affects the efficiency of a remanufacturing scheme design.Aiming to address this problem,a remanufacturing scheme design method based on the reconstruction of incomplete information of used parts is proposed.First,the remaining life of the reconstructed model is predicted by finite element analysis,and the demand for the next life cycle is determined.Second,the scanned 3D damage point cloud data are registered with the original point cloud data using the integral iterative method to construct a missing point cloud model to achieve the restoration of geometric information.Then,according to reverse engineering and laser cladding remanufacturing,the tool remanufacturing process path can be generated by the tool contact point path section line method.Finally,the proposed method is adopted for turbine blades to evaluate the effectiveness and feasibility of the proposed scheme.This study proposes a remanufacturing scheme design method based on the incomplete reconstruction of used part information to solve the uncertain and highly personalized problems in remanufacturing.
基金supported by the National Natural Science Foundation of China(7077111570921001)and Key Project of National Natural Science Foundation of China(70631004)
文摘The weights of criteria are incompletely known and the criteria values are incomplete and uncertain or even default in some fuzzy multi-criteria decision-making problems.For those problems,an approach based on evidential reasoning is proposed,in which the criteria values are integrated on the basis of analytical algorithm of evidential reasoning,and then nonlinear programming models of each alternative are developed with the incomplete information on weights.The genetic algorithm is employed to solve the models,producing the weights and the utility interval of each alternative,and the ranking of the whole set of alternatives can be attained.Finally,an example shows the effectiveness of the method.
文摘The generalized linear model（GLM） based on the observed data with incomplete information in the case of random censorship is defined. Under the given conditions, the existence and uniqueness of the solution on the likelihood equations with respect to the parameter vector β of the model are discussed, and the consistency and asymptotic normality of the maximum likelihood estimator（MLE） βn^-, are proved.
基金This paper is supported by the National Key R&D Program of China(2017YFB0802703)the National Nature Science Foundation of China(61602052).
文摘As a core component of the network,web applications have become one of the preferred targets for attackers because the static configuration of web applications simplifies the exploitation of vulnerabilities by attackers.Although the moving target defense(MTD)has been proposed to increase the attack difficulty for the attackers,there is no solo approach can cope with different attacks;in addition,it is impossible to implement all these approaches simultaneously due to the resource limitation.Thus,the selection of an optimal defense strategy based on MTD has become the focus of research.In general,the confrontation of two players in the security domain is viewed as a stochastic game,and the reward matrices are known to both players.However,in a real security confrontation,this scenario represents an incomplete information game.Each player can only observe the actions performed by the opponent,and the observed actions are not completely accurate.To accurately describe the attacker’s reward function to reach the Nash equilibrium,this work simulated and updated the strategy selection distribution of the attacker by observing and investigating the strategy selection history of the attacker.Next,the possible rewards of the attacker in each confrontation via the observation matrix were corrected.On this basis,the Nash-Q learning algorithm with reward quantification was proposed to select the optimal strategy.Moreover,the performances of the Minimax-Q learning algorithm and Naive-Q learning algorithm were compared and analyzed in the MTD environment.Finally,the experimental results showed that the strategy selection algorithm can enable defenders to select a more reasonable defensive strategy and achieve the maximum possible reward.
基金Projects(10871031, 60474070) supported by the National Natural Science Foundation of ChinaProject(07A001) supported by the Scientific Research Fund of Hunan Provincial Education Department, China
文摘Based on the rough set theory which is a powerful tool in dealing with vagueness and uncertainty, an algorithm to mine association rules in incomplete information systems was presented and the support and confidence were redefined. The algorithm can mine the association rules with decision attributes directly without processing missing values. Using the incomplete dataset Mushroom from UCI machine learning repository, the new algorithm was compared with the classical association rules mining algorithm based on Apriori from the number of rules extracted, testing accuracy and execution time. The experiment results show that the new algorithm has advantages of short execution time and high accuracy.
文摘Incompleteness of information about objects may be the greatest obstruct to performing induction learning from examples. In this paper, the concept of limited-non-symmetric similarity relation is used to formulate a new definition of approximation to an incomplete information system. With the new definition of approximation to an object set and the concept of attribute value pair, rough-setsbased methodology for certain rule acquisition in an incomplete information system is developed. The algorithm can deal with incomplete data directly and does not require changing the size of the original incomplete system. Experiments show that the algorithm provides precise and simple certain decision rules and is not affected by the missing values.
基金Sponsored by the Youth Natural Science Foundation of Yantai Normal University.
文摘It is helpful for people to understand the essence of rough set theory to study the concepts and operations of rough set theory from its information view. In this paper we address knowledge expression and knowledge reduction in incomplete infolvnation systems from the information view of rough set theory. First, by extending information entropy-based measures in complete information systems, two new measures of incomplete entropy and incomplete conditional entropy are presented for incomplete information systems. And then, based on these measures the problem of knowledge reduction in incomplete information systems is analyzed and the reduct definitions in incomplete information system and incomplete decision table are proposed respectively. Finally, the reduct definitions based on incomplete entropy and the reduct definitions based on similarity relation are compared. Two equivalent relationships between them are proved by theorems and an in equivalent relationship between them is illustrated by an example. The work of this paper extends the research of rough set theory from information view to incomplete information systems and establishes the theoretical basis for seeking efficient algorithm of knowledge acquisition in incomplete information systems.
文摘Utilized fundamental theory and analysis method of Incomplete Information repeated games, introduced Incomplete Information into repeated games, and established two stages dynamic games model of the local authority and the coal mine owner. The analytic result indicates that： so long as the country established the corresponding rewards and punishments incentive mechanism to the local authority departments responsible for the work, it reports the safety accident in the coal mine on time. The conclusion that the local government displays right and wrong cooperation behavior will be changed with the introduction of the Incomplete Information. Only has the local authority fulfill their responsibility, can the unsafe accident be controlled effectively. Once this kind of cooperation of local government appears, the costs of the country on the safe supervise and the difficulty will be able to decrease greatly.
基金Supported by the National Natural Science Foundation of China (10771163)
文摘Right randomly censored data with incomplete infor-mation are frequently met in practice.Although much study about right randomly censored data has been seen in the proportional hazards model,relatively little is known about the inference of regression parameters for right randomly censored data with in-complete information in such model.In particular,theoretical properties of the maximum likelihood estimator of the regression parameters have not been proven yet in that model.In this paper,we show the consistency and asymptotic normality of the maxi-mum likelihood estimator of unknown regression parameters.
文摘In this paper, we have discussed a random censoring test with incomplete information, and proved that the maximum likelihood estimator(MLE) of the parameter based on the randomly censored data with incomplete information in the case of the exponential distribution has the strong consistency.
基金This work is supported by the National Young Science Foundation of China (No. 69805004)
文摘In this paper we address information measures of roughness of knowledge and rough sets for incomplete information systems. The definition of rough entropy of knowledge and its important properties are given. In particular, the relationship between rough entropy of knowledge and the Hartley measure of uncertainty is established. We show that rough entropy of know1edge decreases monotonously as granularity of information become smaller. This gives an information interpretation for roughness of knowledge. Based on rough entropy of knowledge and roughness of rough set. a definition of rough entropy of rough set is proposed, and we show that rough entropy of rough set decreases monotonousIy as granularity of information become smaller. This gives more accurate measure for roughness of rough set.
基金supported by the National Natural Science Foundation of China (61403184, 61105082)the ‘1311 Talent Plan’ of Nanjing University of Posts and Telecommunications (NY2013)+3 种基金the ‘Qinglan’ Project of Jiangsu Province (QL2016)the Natural Science Foundation of Nanjing University of Posts and Telecommunications (215149)the Priority Academic Program Development of Jiangsu Higher Education Institutions, (PAPD)the Major Program of the Natural Science Foundation of Jiangsu Province Education Commission (17KJA120001)
文摘To extract and express the knowledge hidden in information systems, discernibility matrix and its extensions were introduced and applied successfully in many real life applications. Binary discernibility matrix, as a representative approach, has many interesting superior properties and has been rapidly developed to find intuitive and easy to understand knowledge. However, at present, the binary discernibility matrix is mainly adopted in the complete information system. It is a challenging topic how to achieve the attribute reduction by using binary discernibility matrix in incomplete information system. A form of generalized binary discernibility matrix is further developed for a number of representative extended rough set models that deal with incomplete information systems. Some useful properties and criteria are introduced for judging the attribute core and attribute relative reduction. Thereafter, a new algorithm is formulated which supports attribute core and attribute relative reduction based on the generalized binary discernibility matrix. This algorithm is not only suitable for consistent information systems but also inconsistent information systems. The feasibility of the proposed methods was demonstrated by worked examples and experimental analysis.
基金supported by Key Program of the NSFC-Guangdong Union Foundation(Grant No.U1135002)Major National S&T Program(Grant No.2011ZX03005-002)+5 种基金National Natural Science Foundation of China(Grant Nos.6087204161072066)Fundamental Research Funds for the Central Universities(Grant Nos.JY10000903001JY10000901034K5051203010)GAD Pre-Research Foundation(Grant No.9140A15040210HK61)
文摘Since web services intended for the same application environment tend to be functionally homogeneous, researchers have turned to their non-functional aspects in order to constructively compare web services and choose an appropriate one. In general, quality of service is very important to users. Many schemes that consider quality assessment have been proposed for web service selection. With the increasing number of qualityrelated attributes, an effective quality assessment method needs satisfactory scalability. Schemes based on the analytic hierarchy process(AHP) meet this requirement. However, prevalent methods in this vein overlook the fact that the traditional AHP needs a complete judgment matrix. In practice, all the information needed to construct a judgment matrix is often unavailable for a variety of reasons, due to which certain judgments cannot be made. In this paper, for an incomplete judgment matrix, we propose an improved AHP approach to consistency verification and the subsequent ordering. Our method can deal with situations where information is insufficient,and inherits all the merits of the traditional AHP approach. A case study establishes the effectiveness of our proposed method.
