Imbalanced datasets are common in practical applications,and oversampling methods using fuzzy rules have been shown to enhance the classification performance of imbalanced data by taking into account the relationship ...Imbalanced datasets are common in practical applications,and oversampling methods using fuzzy rules have been shown to enhance the classification performance of imbalanced data by taking into account the relationship between data attributes.However,the creation of fuzzy rules typically depends on expert knowledge,which may not fully leverage the label information in training data and may be subjective.To address this issue,a novel fuzzy rule oversampling approach is developed based on the learning vector quantization(LVQ)algorithm.In this method,the label information of the training data is utilized to determine the antecedent part of If-Then fuzzy rules by dynamically dividing attribute intervals using LVQ.Subsequently,fuzzy rules are generated and adjusted to calculate rule weights.The number of new samples to be synthesized for each rule is then computed,and samples from the minority class are synthesized based on the newly generated fuzzy rules.This results in the establishment of a fuzzy rule oversampling method based on LVQ.To evaluate the effectiveness of this method,comparative experiments are conducted on 12 publicly available imbalance datasets with five other sampling techniques in combination with the support function machine.The experimental results demonstrate that the proposed method can significantly enhance the classification algorithm across seven performance indicators,including a boost of 2.15%to 12.34%in Accuracy,6.11%to 27.06%in G-mean,and 4.69%to 18.78%in AUC.These show that the proposed method is capable of more efficiently improving the classification performance of imbalanced data.展开更多
By the analysis of CORBA technology, distributed technology, multi agent, fuzzy cluster, OA system, expert system and decision support technology, a distributed OA expert system model based on fuzzy rules (DOAES) is ...By the analysis of CORBA technology, distributed technology, multi agent, fuzzy cluster, OA system, expert system and decision support technology, a distributed OA expert system model based on fuzzy rules (DOAES) is proposed. In DOAES, the knowledge and experience of decision makers are processed and transferred into the knowledge base. So the system has the adaptive ability and re study function and the decision results are more scientific and more objective. The DOAES is successfully applied in the management system of invest promotion.展开更多
There are defects such as the low convergence rate and premature phenomenon on the performance of simple genetic algorithms （SGA） as the values of crossover probability （Pc） and mutation probability （Pro） are fi...There are defects such as the low convergence rate and premature phenomenon on the performance of simple genetic algorithms （SGA） as the values of crossover probability （Pc） and mutation probability （Pro） are fixed. To solve the problems, the fuzzy control method and the genetic algorithms were systematically integrated to create a kind of improved fuzzy adaptive genetic algorithm （FAGA） based on the auto-regulating fuzzy rules （ARFR-FAGA）. By using the fuzzy control method, the values of Pc and Pm were adjusted according to the evolutional process, and the fuzzy rules were optimized by another genetic algorithm. Experimental results in solving the function optimization problems demonstrate that the convergence rate and solution quality of ARFR-FAGA exceed those of SGA, AGA and fuzzy adaptive genetic algorithm based on expertise （EFAGA） obviously in the global search.展开更多
Simultaneous perturbation stochastic approximation （SPSA） belongs to the class of gradient-free optimization methods that extract gradient information from successive objective function evaluation. This paper descri...Simultaneous perturbation stochastic approximation （SPSA） belongs to the class of gradient-free optimization methods that extract gradient information from successive objective function evaluation. This paper describes an improved SPSA algorithm, which entails fuzzy adaptive gain sequences, gradient smoothing, and a step rejection procedure to enhance convergence and stability. The proposed fuzzy adaptive simultaneous perturbation approximation （FASPA） algorithm is particularly well suited to problems involving a large number of parameters such as those encountered in nonlinear system identification using neural networks （NNs）. Accordingly, a multilayer perceptron （MLP） network with popular training algorithms was used to predicate the system response. We found that an MLP trained by FASPSA had the desired accuracy that was comparable to results obtained by traditional system identification algorithms. Simulation results for typical nonlinear systems demonstrate that the proposed NN architecture trained with FASPSA yields improved system identification as measured by reduced time of convergence and a smaller identification error.展开更多
The contamination proposed in this paper is a defect on the surface of ice cream bar,which is a serious security threat.So it is essential to detect this defect before launched on the market. A detection method of con...The contamination proposed in this paper is a defect on the surface of ice cream bar,which is a serious security threat.So it is essential to detect this defect before launched on the market. A detection method of contamination defect on the ice cream bar surface is proposed,which is based on fuzzy rule and absolute neighborhood feature. Firstly,the ice cream bar surface is divided into several sub-regions via the defined adjacent gray level clustering method. Then the alternative contamination regions are extracted from the sub-regions via the defined fuzzy rule. At last,the real contamination regions are recognized via the relationship between absolute neighborhood gray feature and default threshold. The algorithm was tested in the self-built image database SUT-D. The results show that the accuracy of the method proposed in this paper is 97.32 percent,which increases 2.68 percent at least comparing to the other typical algorithms. It indicates that the superiority proposed in this paper,which is of actual use value.展开更多
The face recognition with expression and occlusion variation becomes the greatest challenge in biometric applications to recognize people. The proposed work concentrates on recognizing occlusion and seven kinds of exp...The face recognition with expression and occlusion variation becomes the greatest challenge in biometric applications to recognize people. The proposed work concentrates on recognizing occlusion and seven kinds of expression variations such as neutral, surprise, happy, sad, fear, disgust and angry. During enrollment process, principle component analysis (PCA) detects facial regions on the input image. The detected facial region is converted into fuzzy domain data to make decision during recognition process. The Haar wavelet transform extracts features from the detected facial regions. The Nested Hidden markov model is employed to train these features and each feature of face image is considered as states in a Markov chain to perform learning among the features. The maximum likelihood for the input image was estimated by using Baum Welch algorithm and these features were kept on database. During recognition process, the expression and occlusion varied face image is taken as the test image and maximum likelihood for test image is found by following same procedure done in enrollment process. The matching score between maximum likelihood of input image and test image is computed and it is utilized by fuzzy rule based method to decide whether the test image belongs to authorized or unauthorized. The proposed work was tested among several expression varied and occluded face images of JAFFE and AR datasets respectively.展开更多
As per World Health Organization report which was released in the year of 2019,Diabetes claimed the lives of approximately 1.5 million individuals globally in 2019 and around 450 million people are affected by diabete...As per World Health Organization report which was released in the year of 2019,Diabetes claimed the lives of approximately 1.5 million individuals globally in 2019 and around 450 million people are affected by diabetes all over the world.Hence it is inferred that diabetes is rampant across the world with the majority of the world population being affected by it.Among the diabetics,it can be observed that a large number of people had failed to identify their disease in the initial stage itself and hence the disease level moved from Type-1 to Type-2.To avoid this situation,we propose a new fuzzy logic based neural classifier for early detection of diabetes.A set of new neuro-fuzzy rules is introduced with time constraints that are applied for thefirst level classification.These levels are further refined by using the Fuzzy Cognitive Maps(FCM)with time intervals for making thefinal decision over the classification process.The main objective of this proposed model is to detect the diabetes level based on the time.Also,the set of neuro-fuzzy rules are used for selecting the most contributing values over the decision-making process in diabetes prediction.The proposed model proved its efficiency in performance after experiments conducted not only from the repository but also by using the standard diabetic detection models that are available in the market.展开更多
A fast generation method of fuzzy rules for flux optimization decision-making was proposed in order to extract the linguistic knowledge from numerical data in the process of matter converting. The fuzzy if-then rules ...A fast generation method of fuzzy rules for flux optimization decision-making was proposed in order to extract the linguistic knowledge from numerical data in the process of matter converting. The fuzzy if-then rules with consequent real number were extracted from numerical data, and a linguistic representation method for deriving linguistic rules from fuzzy if-then rules with consequent real numbers was developed. The linguistic representation consisted of The simulat two linguistic variables with the degree of certainty and the storage structure of rule base was described. on results show that the method involves neither the time-consuming iterative learning procedure nor the complicated rule generation mechanisms, and can approximate complex system. The method was applied to determine the flux amount of copper converting furnace in the process of matter converting. The real result shows that the mass fraction of Cu in slag is reduced by 0.5 %.展开更多
This paper firstly proposes a new support vector machine regression (SVR) with a robust loss function, and designs a gradient based algorithm for implementation of the SVR, then uses the SVR to extract fuzzy rules and...This paper firstly proposes a new support vector machine regression (SVR) with a robust loss function, and designs a gradient based algorithm for implementation of the SVR, then uses the SVR to extract fuzzy rules and designs fuzzy rule-based system. Simulations show that fuzzy rule-based system technique based on robust SVR achieves superior performance to the conventional fuzzy inference method, the proposed method provides satisfactory performance with excellent approximation and generalization property than the existing algorithm.展开更多
This paper proposes a support vector machine-based fuzzy rules acquisition system(SVM-FRAS) .The character of SVM in extracting support vector provides a mechanism to extract fuzzy If-Then rules from the training data...This paper proposes a support vector machine-based fuzzy rules acquisition system(SVM-FRAS) .The character of SVM in extracting support vector provides a mechanism to extract fuzzy If-Then rules from the training data set.We construct the fuzzy inference system using fuzzy basis function(FBF) .The gradient technique is used to tune the fuzzy rules and the inference system.Theoretical analysis and comparative tests are performed comparing with other fuzzy systems.Experimental results show the SVM-FRAS model possesses good generalization capability as well as high comprehensibility.展开更多
To deal with the problem that arises when the conventional fuzzy class-association method applies repetitive scans of the classifier to classify new texts,which has low efficiency, a new approach based on the FCR-tree...To deal with the problem that arises when the conventional fuzzy class-association method applies repetitive scans of the classifier to classify new texts,which has low efficiency, a new approach based on the FCR-tree（fuzzy classification rules tree）for text categorization is proposed.The compactness of the FCR-tree saves significant space in storing a large set of rules when there are many repeated words in the rules.In comparison with classification rules,the fuzzy classification rules contain not only words,but also the fuzzy sets corresponding to the frequencies of words appearing in texts.Therefore,the construction of an FCR-tree and its structure are different from a CR-tree.To debase the difficulty of FCR-tree construction and rules retrieval,more k-FCR-trees are built.When classifying a new text,it is not necessary to search the paths of the sub-trees led by those words not appearing in this text,thus reducing the number of traveling rules.Experimental results show that the proposed approach obviously outperforms the conventional method in efficiency.展开更多
Breast cancer remains a significant global health challenge, necessitating effective early detection and prognosis to enhance patient outcomes. Current diagnostic methods, including mammography and MRI, suffer from li...Breast cancer remains a significant global health challenge, necessitating effective early detection and prognosis to enhance patient outcomes. Current diagnostic methods, including mammography and MRI, suffer from limitations such as uncertainty and imprecise data, leading to late-stage diagnoses. To address this, various expert systems have been developed, but many rely on type-1 fuzzy logic and lack mobile-based applications for data collection and feedback to healthcare practitioners. This research investigates the development of an Enhanced Mobile-based Fuzzy Expert system (EMFES) for breast cancer pre-growth prognosis. The study explores the use of type-2 fuzzy logic to enhance accuracy and model uncertainty effectively. Additionally, it evaluates the advantages of employing the python programming language over java for implementation and considers specific risk factors for data collection. The research aims to dynamically generate fuzzy rules, adapting to evolving breast cancer research and patient data. Key research questions focus on the comparative effectiveness of type-2 fuzzy logic, the handling of uncertainty and imprecise data, the integration of mobile-based features, the choice of programming language, and the creation of dynamic fuzzy rules. Furthermore, the study examines the differences between the Mamdani Inference System and the Sugeno Fuzzy Inference method and explores challenges and opportunities in deploying the EMFES on mobile devices. The research identifies a critical gap in existing breast cancer diagnostic systems, emphasizing the need for a comprehensive, mobile-enabled, and adaptable solution by developing an EMFES that leverages Type-2 fuzzy logic, the Sugeno Inference Algorithm, Python Programming, and dynamic fuzzy rule generation. This study seeks to enhance early breast cancer detection and ultimately reduce breast cancer-related mortality.展开更多
This paper presents the construction of an active suspension control of a one-wheel car model using fuzzy reasoning and a disturbance observer. The one-wheel car model to be treated here can be approximately described...This paper presents the construction of an active suspension control of a one-wheel car model using fuzzy reasoning and a disturbance observer. The one-wheel car model to be treated here can be approximately described as a nonlinear two degrees of freedom system subject to excitation from a road profile. The active control is designed as the fuzzy control inferred by using single input rule modules fuzzy reasoning, and the active control force is released by actuating a pneumatic actuator. The excitation from the road profile is estimated by using a disturbance observer, and the estimate is denoted as one of the variables in the precondition part of the fuzzy control rules. A compensator is inserted to counter the performance degradation due to the delay of the pneumatic actuator. The experimental result indicates that the proposed active suspension system improves much the vibration suppression of the car model. Key words One-wheel car model - Active suspension system - Single input rule modules fuzzy reasoning - Pneumatic actuator - Disturbance observer Document code A CLC number TH16展开更多
DDoS(Distributed Denial of Service)attack is being the most extensive danger and difficulty to defense.A new kind of DDoS attack named DRDoS(Distributed Reflector Denial of Service)appears in recent years,which is mor...DDoS(Distributed Denial of Service)attack is being the most extensive danger and difficulty to defense.A new kind of DDoS attack named DRDoS(Distributed Reflector Denial of Service)appears in recent years,which is more dangerous than DDoS attack because it is in stronger disguise.In this paper,the principle of DRDoS attack is studied and the network traffic is analyzed by fuzzy association rules with path restricted when DRDoS attack happens.And the association rules about spoofed network traffic and attack network traffic are mined.Also the DRDoS Attack Defensive Architecture based on Multi-Agent(D2AMA)is set up in order to realize the detection,orientation and defensive function.D2AMA is validated by NS-2 platform.It can detect and orient the attack source in a short time,realize scrvvning the attack source and stop transmitting attack traffic.The experimental result proves that D2AMA can find out the attack,orient the attack source and hold out the attack effectively.展开更多
At present, most of the association rules algorithms are based on the Boolean attribute and single-level association rules mining. But data of the real world has various types, the multi-level and quantitative attribu...At present, most of the association rules algorithms are based on the Boolean attribute and single-level association rules mining. But data of the real world has various types, the multi-level and quantitative attributes are got more and more attention. And the most important step is to mine frequent sets. In this paper, we propose an algorithm that is called fuzzy multiple-level association （FMA） rules to mine frequent sets. It is based on the improved Eclat algorithm that is different to many researchers’ proposed algorithms thatused the Apriori algorithm. We analyze quantitative data’s frequent sets by using the fuzzy theory, dividing the hierarchy of concept and softening the boundary of attributes’ values and frequency. In this paper, we use the vertical-style data and the improved Eclat algorithm to describe the proposed method, we use this algorithm to analyze the data of Beijing logistics route. Experiments show that the algorithm has a good performance, it has better effectiveness and high efficiency.展开更多
To improve the ability and precisions of the fuzzy control,this thesis points out the adjusted fuzzy control method,realizes the precision of the fuzzy quantity, and reduces the number of the fuzzy control rules,so th...To improve the ability and precisions of the fuzzy control,this thesis points out the adjusted fuzzy control method,realizes the precision of the fuzzy quantity, and reduces the number of the fuzzy control rules,so that it can predigest the process of disigns and realize the methods without influencing the idiocratic control,which are on the base of the domain flexing.展开更多
The design target with definite purpose character of product quality wasdescribed in a real fuzzy number ( named fuzzy target for short in this paper), and its membershipjunctions in common use were given. According t...The design target with definite purpose character of product quality wasdescribed in a real fuzzy number ( named fuzzy target for short in this paper), and its membershipjunctions in common use were given. According to the fuzzy probability theory and the robust designprinciple, the robust design rule based on fuzzy probability (named fuzzy robust design rule forshort) was put forward and its validity and practicability were analyzed and tested with a designexample. The theoretical analysis and the design examples make clear that, while the fuzzy robustdesign rule was used, the fine design effect can be obtained and the fuzzy robust design rule can bevery suitable for the choice of the membership function of the fuzzy target; so it has a particularadvantage.展开更多
Since the traditional Miner rule ignores the influence of the load sequence on the fatigue life, the fuzzy rules are used to analyze the fuzziness of the fatigue damage caused by the stress nearby the fatigue limit un...Since the traditional Miner rule ignores the influence of the load sequence on the fatigue life, the fuzzy rules are used to analyze the fuzziness of the fatigue damage caused by the stress nearby the fatigue limit under different load sequences. The improved fuzzy Miner rule can reflect the influence of the load sequence on the fatigue life. Results of the example show that the prediction error can be reduced from 61.6% to 21.7%.展开更多
Using electric motors instead of diesel engines as the driving system for mining excavators can reduce the energy consumption and operating costs.However,pure electric-driven mining excavators are prone to unexpected ...Using electric motors instead of diesel engines as the driving system for mining excavators can reduce the energy consumption and operating costs.However,pure electric-driven mining excavators are prone to unexpected power outages in mines because of drastic changes in load power,leading to significant fluctuations in the power demand of the grid,which in turn affects production.To solve the above problem,a pure electric-driven mining hydraulic excavator based on electric-motor-driven swing platform and hydraulic pumps was used as the research object.Moreover,supercapacitors and DC/DC converter,as the energy storage system(ESS)adjust the output power of the grid and recover the braking kinetic energy of the swing platform.Subsequently,a novel integrated energy management strategy for a DC bus voltage predictive controller based on the power feedforward of fuzzy rules is proposed to run mining excavators efficiently and reliably.Specifically,the working modes of the ESS are determined by the DC bus voltage and state of charge(SOC)of the supercapacitor.Next,the output power of the supercapacitor and the DC bus voltage were controlled by adjusting the charging and discharging currents of the DC/DC converter using a predictive controller and fuzzy rules.In addition,a digital prototype of the excavator was verified using an original machine test.The performance of the different strategies and driven systems were analyzed using digital prototypes.The results showed that,compared with traditional excavators with diesel engines,the operational cost of the developed excavators was reduced by 54.02%.Compared to pure electric-driven excavators without an ESS,the peak power of the grid for the developed excavators was reduced by 10%.This study designed an integrated energy management strategy for a pure electric mining excavator that can regulate the power output of the grid and maintain the stability of the bus voltage and SOC of the ESS.展开更多
Hydrogen fuel cell ships are one of the key solutions to achieving zero carbon emissions in shipping.Multi-fuel cell stacks(MFCS)systems are frequently employed to fulfill the power requirements of high-load power equ...Hydrogen fuel cell ships are one of the key solutions to achieving zero carbon emissions in shipping.Multi-fuel cell stacks(MFCS)systems are frequently employed to fulfill the power requirements of high-load power equipment on ships.Compared to single-stack system,MFCS may be difficult to apply traditional energy management strategies(EMS)due to their complex structure.In this paper,a two-layer power allocation strategy for MFCS of a hydrogen fuel cell ship is proposed to reduce the complexity of the allocation task by splitting it into each layer of the EMS.The first layer of the EMSis centered on the Nonlinear Model Predictive Control(NMPC).The Northern Goshawk Optimization(NGO)algorithm is used to solve the nonlinear optimization problem in NMPC,and the local fine search is performed using sequential quadratic programming(SQP).Based on the power allocation results of the first layer,the second layer is centered on a fuzzy rule-based adaptive power allocation strategy(AP-Fuzzy).The membership function bounds of the fuzzy controller are related to the aging level of the MFCS.The Particle Swarm Optimization(PSO)algorithm is used to optimize the parameters of the residual membership function to improve the performance of the proposed strategy.The effectiveness of the proposed EMS is verified by comparing it with the traditional EMS.The experimental results show that the EMS proposed in this paper can ensure reasonable hydrogen consumption,slow down the FC aging and equalize its performance,effectively extend the system life,and ensure that the ship has good endurance after completing the mission.展开更多
基金funded by the National Science Foundation of China(62006068)Hebei Natural Science Foundation(A2021402008),Natural Science Foundation of Scientific Research Project of Higher Education in Hebei Province(ZD2020185,QN2020188)333 Talent Supported Project of Hebei Province(C20221026).
文摘Imbalanced datasets are common in practical applications,and oversampling methods using fuzzy rules have been shown to enhance the classification performance of imbalanced data by taking into account the relationship between data attributes.However,the creation of fuzzy rules typically depends on expert knowledge,which may not fully leverage the label information in training data and may be subjective.To address this issue,a novel fuzzy rule oversampling approach is developed based on the learning vector quantization(LVQ)algorithm.In this method,the label information of the training data is utilized to determine the antecedent part of If-Then fuzzy rules by dynamically dividing attribute intervals using LVQ.Subsequently,fuzzy rules are generated and adjusted to calculate rule weights.The number of new samples to be synthesized for each rule is then computed,and samples from the minority class are synthesized based on the newly generated fuzzy rules.This results in the establishment of a fuzzy rule oversampling method based on LVQ.To evaluate the effectiveness of this method,comparative experiments are conducted on 12 publicly available imbalance datasets with five other sampling techniques in combination with the support function machine.The experimental results demonstrate that the proposed method can significantly enhance the classification algorithm across seven performance indicators,including a boost of 2.15%to 12.34%in Accuracy,6.11%to 27.06%in G-mean,and 4.69%to 18.78%in AUC.These show that the proposed method is capable of more efficiently improving the classification performance of imbalanced data.
文摘By the analysis of CORBA technology, distributed technology, multi agent, fuzzy cluster, OA system, expert system and decision support technology, a distributed OA expert system model based on fuzzy rules (DOAES) is proposed. In DOAES, the knowledge and experience of decision makers are processed and transferred into the knowledge base. So the system has the adaptive ability and re study function and the decision results are more scientific and more objective. The DOAES is successfully applied in the management system of invest promotion.
基金Project(60574030) supported by the National Natural Science Foundation of ChinaKey Project(60634020) supported by the National Natural Science Foundation of China
文摘There are defects such as the low convergence rate and premature phenomenon on the performance of simple genetic algorithms （SGA） as the values of crossover probability （Pc） and mutation probability （Pro） are fixed. To solve the problems, the fuzzy control method and the genetic algorithms were systematically integrated to create a kind of improved fuzzy adaptive genetic algorithm （FAGA） based on the auto-regulating fuzzy rules （ARFR-FAGA）. By using the fuzzy control method, the values of Pc and Pm were adjusted according to the evolutional process, and the fuzzy rules were optimized by another genetic algorithm. Experimental results in solving the function optimization problems demonstrate that the convergence rate and solution quality of ARFR-FAGA exceed those of SGA, AGA and fuzzy adaptive genetic algorithm based on expertise （EFAGA） obviously in the global search.
文摘Simultaneous perturbation stochastic approximation （SPSA） belongs to the class of gradient-free optimization methods that extract gradient information from successive objective function evaluation. This paper describes an improved SPSA algorithm, which entails fuzzy adaptive gain sequences, gradient smoothing, and a step rejection procedure to enhance convergence and stability. The proposed fuzzy adaptive simultaneous perturbation approximation （FASPA） algorithm is particularly well suited to problems involving a large number of parameters such as those encountered in nonlinear system identification using neural networks （NNs）. Accordingly, a multilayer perceptron （MLP） network with popular training algorithms was used to predicate the system response. We found that an MLP trained by FASPSA had the desired accuracy that was comparable to results obtained by traditional system identification algorithms. Simulation results for typical nonlinear systems demonstrate that the proposed NN architecture trained with FASPSA yields improved system identification as measured by reduced time of convergence and a smaller identification error.
文摘The contamination proposed in this paper is a defect on the surface of ice cream bar,which is a serious security threat.So it is essential to detect this defect before launched on the market. A detection method of contamination defect on the ice cream bar surface is proposed,which is based on fuzzy rule and absolute neighborhood feature. Firstly,the ice cream bar surface is divided into several sub-regions via the defined adjacent gray level clustering method. Then the alternative contamination regions are extracted from the sub-regions via the defined fuzzy rule. At last,the real contamination regions are recognized via the relationship between absolute neighborhood gray feature and default threshold. The algorithm was tested in the self-built image database SUT-D. The results show that the accuracy of the method proposed in this paper is 97.32 percent,which increases 2.68 percent at least comparing to the other typical algorithms. It indicates that the superiority proposed in this paper,which is of actual use value.
文摘The face recognition with expression and occlusion variation becomes the greatest challenge in biometric applications to recognize people. The proposed work concentrates on recognizing occlusion and seven kinds of expression variations such as neutral, surprise, happy, sad, fear, disgust and angry. During enrollment process, principle component analysis (PCA) detects facial regions on the input image. The detected facial region is converted into fuzzy domain data to make decision during recognition process. The Haar wavelet transform extracts features from the detected facial regions. The Nested Hidden markov model is employed to train these features and each feature of face image is considered as states in a Markov chain to perform learning among the features. The maximum likelihood for the input image was estimated by using Baum Welch algorithm and these features were kept on database. During recognition process, the expression and occlusion varied face image is taken as the test image and maximum likelihood for test image is found by following same procedure done in enrollment process. The matching score between maximum likelihood of input image and test image is computed and it is utilized by fuzzy rule based method to decide whether the test image belongs to authorized or unauthorized. The proposed work was tested among several expression varied and occluded face images of JAFFE and AR datasets respectively.
文摘As per World Health Organization report which was released in the year of 2019,Diabetes claimed the lives of approximately 1.5 million individuals globally in 2019 and around 450 million people are affected by diabetes all over the world.Hence it is inferred that diabetes is rampant across the world with the majority of the world population being affected by it.Among the diabetics,it can be observed that a large number of people had failed to identify their disease in the initial stage itself and hence the disease level moved from Type-1 to Type-2.To avoid this situation,we propose a new fuzzy logic based neural classifier for early detection of diabetes.A set of new neuro-fuzzy rules is introduced with time constraints that are applied for thefirst level classification.These levels are further refined by using the Fuzzy Cognitive Maps(FCM)with time intervals for making thefinal decision over the classification process.The main objective of this proposed model is to detect the diabetes level based on the time.Also,the set of neuro-fuzzy rules are used for selecting the most contributing values over the decision-making process in diabetes prediction.The proposed model proved its efficiency in performance after experiments conducted not only from the repository but also by using the standard diabetic detection models that are available in the market.
基金Project(50374079) supported bythe National Natural Science Foundation of China project(2002cB312200) supported bythe State Key Fundamental Research and Development Programof China
文摘A fast generation method of fuzzy rules for flux optimization decision-making was proposed in order to extract the linguistic knowledge from numerical data in the process of matter converting. The fuzzy if-then rules with consequent real number were extracted from numerical data, and a linguistic representation method for deriving linguistic rules from fuzzy if-then rules with consequent real numbers was developed. The linguistic representation consisted of The simulat two linguistic variables with the degree of certainty and the storage structure of rule base was described. on results show that the method involves neither the time-consuming iterative learning procedure nor the complicated rule generation mechanisms, and can approximate complex system. The method was applied to determine the flux amount of copper converting furnace in the process of matter converting. The real result shows that the mass fraction of Cu in slag is reduced by 0.5 %.
基金Supported by Zhejiang Province Nature Science Fund (No.Y106259)
文摘This paper firstly proposes a new support vector machine regression (SVR) with a robust loss function, and designs a gradient based algorithm for implementation of the SVR, then uses the SVR to extract fuzzy rules and designs fuzzy rule-based system. Simulations show that fuzzy rule-based system technique based on robust SVR achieves superior performance to the conventional fuzzy inference method, the proposed method provides satisfactory performance with excellent approximation and generalization property than the existing algorithm.
基金the Shanghai Sciences and Technology Committee under Grant No.08DZ1202500 (No.08DZ1202502)the Young Faculty Research Grant of Shanghai Maritime Universitythe Shanghai Young Faculty Research Grant (No.shs08032)
文摘This paper proposes a support vector machine-based fuzzy rules acquisition system(SVM-FRAS) .The character of SVM in extracting support vector provides a mechanism to extract fuzzy If-Then rules from the training data set.We construct the fuzzy inference system using fuzzy basis function(FBF) .The gradient technique is used to tune the fuzzy rules and the inference system.Theoretical analysis and comparative tests are performed comparing with other fuzzy systems.Experimental results show the SVM-FRAS model possesses good generalization capability as well as high comprehensibility.
基金The National Natural Science Foundation of China(No.60473045)the Technology Research Project of Hebei Province(No.05213573)the Research Plan of Education Office of Hebei Province(No.2004406)
文摘To deal with the problem that arises when the conventional fuzzy class-association method applies repetitive scans of the classifier to classify new texts,which has low efficiency, a new approach based on the FCR-tree（fuzzy classification rules tree）for text categorization is proposed.The compactness of the FCR-tree saves significant space in storing a large set of rules when there are many repeated words in the rules.In comparison with classification rules,the fuzzy classification rules contain not only words,but also the fuzzy sets corresponding to the frequencies of words appearing in texts.Therefore,the construction of an FCR-tree and its structure are different from a CR-tree.To debase the difficulty of FCR-tree construction and rules retrieval,more k-FCR-trees are built.When classifying a new text,it is not necessary to search the paths of the sub-trees led by those words not appearing in this text,thus reducing the number of traveling rules.Experimental results show that the proposed approach obviously outperforms the conventional method in efficiency.
文摘Breast cancer remains a significant global health challenge, necessitating effective early detection and prognosis to enhance patient outcomes. Current diagnostic methods, including mammography and MRI, suffer from limitations such as uncertainty and imprecise data, leading to late-stage diagnoses. To address this, various expert systems have been developed, but many rely on type-1 fuzzy logic and lack mobile-based applications for data collection and feedback to healthcare practitioners. This research investigates the development of an Enhanced Mobile-based Fuzzy Expert system (EMFES) for breast cancer pre-growth prognosis. The study explores the use of type-2 fuzzy logic to enhance accuracy and model uncertainty effectively. Additionally, it evaluates the advantages of employing the python programming language over java for implementation and considers specific risk factors for data collection. The research aims to dynamically generate fuzzy rules, adapting to evolving breast cancer research and patient data. Key research questions focus on the comparative effectiveness of type-2 fuzzy logic, the handling of uncertainty and imprecise data, the integration of mobile-based features, the choice of programming language, and the creation of dynamic fuzzy rules. Furthermore, the study examines the differences between the Mamdani Inference System and the Sugeno Fuzzy Inference method and explores challenges and opportunities in deploying the EMFES on mobile devices. The research identifies a critical gap in existing breast cancer diagnostic systems, emphasizing the need for a comprehensive, mobile-enabled, and adaptable solution by developing an EMFES that leverages Type-2 fuzzy logic, the Sugeno Inference Algorithm, Python Programming, and dynamic fuzzy rule generation. This study seeks to enhance early breast cancer detection and ultimately reduce breast cancer-related mortality.
文摘This paper presents the construction of an active suspension control of a one-wheel car model using fuzzy reasoning and a disturbance observer. The one-wheel car model to be treated here can be approximately described as a nonlinear two degrees of freedom system subject to excitation from a road profile. The active control is designed as the fuzzy control inferred by using single input rule modules fuzzy reasoning, and the active control force is released by actuating a pneumatic actuator. The excitation from the road profile is estimated by using a disturbance observer, and the estimate is denoted as one of the variables in the precondition part of the fuzzy control rules. A compensator is inserted to counter the performance degradation due to the delay of the pneumatic actuator. The experimental result indicates that the proposed active suspension system improves much the vibration suppression of the car model. Key words One-wheel car model - Active suspension system - Single input rule modules fuzzy reasoning - Pneumatic actuator - Disturbance observer Document code A CLC number TH16
文摘DDoS(Distributed Denial of Service)attack is being the most extensive danger and difficulty to defense.A new kind of DDoS attack named DRDoS(Distributed Reflector Denial of Service)appears in recent years,which is more dangerous than DDoS attack because it is in stronger disguise.In this paper,the principle of DRDoS attack is studied and the network traffic is analyzed by fuzzy association rules with path restricted when DRDoS attack happens.And the association rules about spoofed network traffic and attack network traffic are mined.Also the DRDoS Attack Defensive Architecture based on Multi-Agent(D2AMA)is set up in order to realize the detection,orientation and defensive function.D2AMA is validated by NS-2 platform.It can detect and orient the attack source in a short time,realize scrvvning the attack source and stop transmitting attack traffic.The experimental result proves that D2AMA can find out the attack,orient the attack source and hold out the attack effectively.
基金supported by the Fundamental Research Funds for the Central Universities under Grants No.ZYGX2014J051 and No.ZYGX2014J066Science and Technology Projects in Sichuan Province under Grants No.2015JY0178,No.2016FZ0002,No.2014GZ0109,No.2015KZ002 and No.2015JY0030China Postdoctoral Science Foundation under Grant No.2015M572464
文摘At present, most of the association rules algorithms are based on the Boolean attribute and single-level association rules mining. But data of the real world has various types, the multi-level and quantitative attributes are got more and more attention. And the most important step is to mine frequent sets. In this paper, we propose an algorithm that is called fuzzy multiple-level association （FMA） rules to mine frequent sets. It is based on the improved Eclat algorithm that is different to many researchers’ proposed algorithms thatused the Apriori algorithm. We analyze quantitative data’s frequent sets by using the fuzzy theory, dividing the hierarchy of concept and softening the boundary of attributes’ values and frequency. In this paper, we use the vertical-style data and the improved Eclat algorithm to describe the proposed method, we use this algorithm to analyze the data of Beijing logistics route. Experiments show that the algorithm has a good performance, it has better effectiveness and high efficiency.
文摘To improve the ability and precisions of the fuzzy control,this thesis points out the adjusted fuzzy control method,realizes the precision of the fuzzy quantity, and reduces the number of the fuzzy control rules,so that it can predigest the process of disigns and realize the methods without influencing the idiocratic control,which are on the base of the domain flexing.
文摘The design target with definite purpose character of product quality wasdescribed in a real fuzzy number ( named fuzzy target for short in this paper), and its membershipjunctions in common use were given. According to the fuzzy probability theory and the robust designprinciple, the robust design rule based on fuzzy probability (named fuzzy robust design rule forshort) was put forward and its validity and practicability were analyzed and tested with a designexample. The theoretical analysis and the design examples make clear that, while the fuzzy robustdesign rule was used, the fine design effect can be obtained and the fuzzy robust design rule can bevery suitable for the choice of the membership function of the fuzzy target; so it has a particularadvantage.
基金the National Natural Science Foundation of China(60472118)~~
文摘Since the traditional Miner rule ignores the influence of the load sequence on the fatigue life, the fuzzy rules are used to analyze the fuzziness of the fatigue damage caused by the stress nearby the fatigue limit under different load sequences. The improved fuzzy Miner rule can reflect the influence of the load sequence on the fatigue life. Results of the example show that the prediction error can be reduced from 61.6% to 21.7%.
基金Supported by National Natural Science Foundation of ChinaShanxi Coalbased Low-Carbon Joint Fund(Grant No.U1910211)。
文摘Using electric motors instead of diesel engines as the driving system for mining excavators can reduce the energy consumption and operating costs.However,pure electric-driven mining excavators are prone to unexpected power outages in mines because of drastic changes in load power,leading to significant fluctuations in the power demand of the grid,which in turn affects production.To solve the above problem,a pure electric-driven mining hydraulic excavator based on electric-motor-driven swing platform and hydraulic pumps was used as the research object.Moreover,supercapacitors and DC/DC converter,as the energy storage system(ESS)adjust the output power of the grid and recover the braking kinetic energy of the swing platform.Subsequently,a novel integrated energy management strategy for a DC bus voltage predictive controller based on the power feedforward of fuzzy rules is proposed to run mining excavators efficiently and reliably.Specifically,the working modes of the ESS are determined by the DC bus voltage and state of charge(SOC)of the supercapacitor.Next,the output power of the supercapacitor and the DC bus voltage were controlled by adjusting the charging and discharging currents of the DC/DC converter using a predictive controller and fuzzy rules.In addition,a digital prototype of the excavator was verified using an original machine test.The performance of the different strategies and driven systems were analyzed using digital prototypes.The results showed that,compared with traditional excavators with diesel engines,the operational cost of the developed excavators was reduced by 54.02%.Compared to pure electric-driven excavators without an ESS,the peak power of the grid for the developed excavators was reduced by 10%.This study designed an integrated energy management strategy for a pure electric mining excavator that can regulate the power output of the grid and maintain the stability of the bus voltage and SOC of the ESS.
基金supported by the National Key R&D Program of China(2022YFB4301403).
文摘Hydrogen fuel cell ships are one of the key solutions to achieving zero carbon emissions in shipping.Multi-fuel cell stacks(MFCS)systems are frequently employed to fulfill the power requirements of high-load power equipment on ships.Compared to single-stack system,MFCS may be difficult to apply traditional energy management strategies(EMS)due to their complex structure.In this paper,a two-layer power allocation strategy for MFCS of a hydrogen fuel cell ship is proposed to reduce the complexity of the allocation task by splitting it into each layer of the EMS.The first layer of the EMSis centered on the Nonlinear Model Predictive Control(NMPC).The Northern Goshawk Optimization(NGO)algorithm is used to solve the nonlinear optimization problem in NMPC,and the local fine search is performed using sequential quadratic programming(SQP).Based on the power allocation results of the first layer,the second layer is centered on a fuzzy rule-based adaptive power allocation strategy(AP-Fuzzy).The membership function bounds of the fuzzy controller are related to the aging level of the MFCS.The Particle Swarm Optimization(PSO)algorithm is used to optimize the parameters of the residual membership function to improve the performance of the proposed strategy.The effectiveness of the proposed EMS is verified by comparing it with the traditional EMS.The experimental results show that the EMS proposed in this paper can ensure reasonable hydrogen consumption,slow down the FC aging and equalize its performance,effectively extend the system life,and ensure that the ship has good endurance after completing the mission.
