In this paper,we propose a neural network approach to learn the parameters of a class of stochastic Lotka-Volterra systems.Approximations of the mean and covariance matrix of the observational variables are obtained f...In this paper,we propose a neural network approach to learn the parameters of a class of stochastic Lotka-Volterra systems.Approximations of the mean and covariance matrix of the observational variables are obtained from the Euler-Maruyama discretization of the underlying stochastic differential equations(SDEs),based on which the loss function is built.The stochastic gradient descent method is applied in the neural network training.Numerical experiments demonstrate the effectiveness of our method.展开更多
Stochastic resonance can utilize the energy of noise to enhance weak frequency characteristic.This paper proposes an adaptive multi-stable stochastic resonance method assisted by the neural network(NN)and physics supe...Stochastic resonance can utilize the energy of noise to enhance weak frequency characteristic.This paper proposes an adaptive multi-stable stochastic resonance method assisted by the neural network(NN)and physics supervision(directly numerical simulation of the physical system).Different from traditional adaptive algorithm,the evaluation of the objective function(i.e.,fitness function)in iteration process of adaptive algorithm is through a trained neural network instead of the numerical simulation.It will bring a dramatically reduction in computation time.Considering predictive bias from the neural network,a secondary correction procedure is introduced to the reevaluate the top performers and then resort them in iteration process through physics supervision.Though it may increase the computing cost,the accuracy will be enhanced.Two examples are given to illustrate the proposed method.For a classical multi-stable stochastic resonance system,the results show that the proposed method not only amplifies weak signals effectively but also significantly reduces computing time.For the detection of weak signal from outer ring in bearings,by introducing a variable scale coefficient,the proposed method can also give a satisfactory result,and the characteristic frequency of the fault signal can be extracted correctly.展开更多
This study proposes a novel time-synchronization protocol inspired by stochastic gradient algorithms.The clock model of each network node in this synchronizer is configured as a generic adaptive filter where different...This study proposes a novel time-synchronization protocol inspired by stochastic gradient algorithms.The clock model of each network node in this synchronizer is configured as a generic adaptive filter where different stochastic gradient algorithms can be adopted for adaptive clock frequency adjustments.The study analyzes the pairwise synchronization behavior of the protocol and proves the generalized convergence of the synchronization error and clock frequency.A novel closed-form expression is also derived for a generalized asymptotic error variance steady state.Steady and convergence analyses are then presented for the synchronization,with frequency adaptations done using least mean square(LMS),the Newton search,the gradient descent(GraDes),the normalized LMS(N-LMS),and the Sign-Data LMS algorithms.Results obtained from real-time experiments showed a better performance of our protocols as compared to the Average Proportional-Integral Synchronization Protocol(AvgPISync)regarding the impact of quantization error on synchronization accuracy,precision,and convergence time.This generalized approach to time synchronization allows flexibility in selecting a suitable protocol for different wireless sensor network applications.展开更多
Wireless sensor network deployment optimization is a classic NP-hard problem and a popular topic in academic research.However,the current research on wireless sensor network deployment problems uses overly simplistic ...Wireless sensor network deployment optimization is a classic NP-hard problem and a popular topic in academic research.However,the current research on wireless sensor network deployment problems uses overly simplistic models,and there is a significant gap between the research results and actual wireless sensor networks.Some scholars have now modeled data fusion networks to make them more suitable for practical applications.This paper will explore the deployment problem of a stochastic data fusion wireless sensor network(SDFWSN),a model that reflects the randomness of environmental monitoring and uses data fusion techniques widely used in actual sensor networks for information collection.The deployment problem of SDFWSN is modeled as a multi-objective optimization problem.The network life cycle,spatiotemporal coverage,detection rate,and false alarm rate of SDFWSN are used as optimization objectives to optimize the deployment of network nodes.This paper proposes an enhanced multi-objective mongoose optimization algorithm(EMODMOA)to solve the deployment problem of SDFWSN.First,to overcome the shortcomings of the DMOA algorithm,such as its low convergence and tendency to get stuck in a local optimum,an encircling and hunting strategy is introduced into the original algorithm to propose the EDMOA algorithm.The EDMOA algorithm is designed as the EMODMOA algorithm by selecting reference points using the K-Nearest Neighbor(KNN)algorithm.To verify the effectiveness of the proposed algorithm,the EMODMOA algorithm was tested at CEC 2020 and achieved good results.In the SDFWSN deployment problem,the algorithm was compared with the Non-dominated Sorting Genetic Algorithm II(NSGAII),Multiple Objective Particle Swarm Optimization(MOPSO),Multi-Objective Evolutionary Algorithm based on Decomposition(MOEA/D),and Multi-Objective Grey Wolf Optimizer(MOGWO).By comparing and analyzing the performance evaluation metrics and optimization results of the objective functions of the multi-objective algorithms,the algorithm outperforms the other algorithms in the SDFWSN deployment results.To better demonstrate the superiority of the algorithm,simulations of diverse test cases were also performed,and good results were obtained.展开更多
Complex industrial processes present typical uncertainty due to fluctuations in the composition of raw materials and frequently changing operating conditions.This poses three challenges for precise fault diagnosis,inc...Complex industrial processes present typical uncertainty due to fluctuations in the composition of raw materials and frequently changing operating conditions.This poses three challenges for precise fault diagnosis,including random noise interference,less distinguishability between multi-class faults,and the new fault emerging.To address these issues,this study formulates fault diagnosis in uncertain industrial processes as a multilevel refined fault diagnosis problem.A hierarchical stochastic network approach is proposed to refine fault diagnosis of multiclass faults.This method considers the augmentation of fault categories as naturally following a hierarchical structure.At each hierarchical stage,stochastic network methods are designed according to the sources of uncertainty.For fault feature extraction,a doubly stochastic attention-based variational graph autoencoder is introduced to suppress noise during the messagepassing process,ensuring the extraction of high-quality fault features and providing the provision of differentiated information.Subsequently,multiple stochastic configuration networks are deployed to realize multi-level fault diagnosis from coarse to fine granularity via a hierarchical structure rather than treating all faults equally.This approach effectively enhances the precision of multi-class fault diagnosis and ensures its robust generalization capability.Finally,the feasibility and effectiveness of the proposed method are validated using two industrial processes.The results demonstrate that the proposed method can effectively suppress the random noise interference and adapt to the emergence of small samples and imbalanced extreme fault-type data,achieving a satisfactory fault diagnosis performance.展开更多
This paper proposes a reliability evaluation model for a multi-dimensional network system,which has potential to be applied to the internet of things or other practical networks.A multi-dimensional network system with...This paper proposes a reliability evaluation model for a multi-dimensional network system,which has potential to be applied to the internet of things or other practical networks.A multi-dimensional network system with one source element and multiple sink elements is considered first.Each element can con-nect with other elements within a stochastic connection ranges.The system is regarded as successful as long as the source ele-ment remains connected with all sink elements.An importance measure is proposed to evaluate the performance of non-source elements.Furthermore,to calculate the system reliability and the element importance measure,a multi-valued decision diagram based approach is structured and its complexity is analyzed.Finally,a numerical example about the signal transfer station system is illustrated to analyze the system reliability and the ele-ment importance measure.展开更多
In this paper,we use a direct method to study the almost periodic dynamics of an octonion-valued stochastic shunting inhibitory cellular neural network with variable delays.By using the fixed point method and inequali...In this paper,we use a direct method to study the almost periodic dynamics of an octonion-valued stochastic shunting inhibitory cellular neural network with variable delays.By using the fixed point method and inequality technique,the existence,uniqueness and stability of almost periodic solutions in the sense of distribution of the neural network under consideration are obtained.Our results are brand new.展开更多
To improve the generalization performance and prediction accuracy of the stochastic configuration network(SCN)model,a novel SCN modeling method is proposed.First,the first-and second-order directional derivatives of t...To improve the generalization performance and prediction accuracy of the stochastic configuration network(SCN)model,a novel SCN modeling method is proposed.First,the first-and second-order directional derivatives of the hidden layer output matrix are calculated.The key factors extracted from the directional derivatives are linearly added to the original hidden layer output matrix to formulate a new hidden layer output matrix.Second,a spatial angle adaptive supervisory mechanism is established to improve the quality of the parameter configuration of the hidden layer nodes.The experimental results show that the proposed method improves the generalization performance and prediction accuracy.This work is a beneficial exploration of the standard SCN algorithm.展开更多
Enhancing the accuracy of real-time ship roll prediction is crucial for maritime safety and operational efficiency.To address the challenge of accurately predicting the ship roll status with nonlinear time-varying dyn...Enhancing the accuracy of real-time ship roll prediction is crucial for maritime safety and operational efficiency.To address the challenge of accurately predicting the ship roll status with nonlinear time-varying dynamic characteristics,a real-time ship roll prediction scheme is proposed on the basis of a data preprocessing strategy and a novel stochastic trainer-based feedforward neural network.The sliding data window serves as a ship time-varying dynamic observer to enhance model prediction stability.The variational mode decomposition method extracts effective information on ship roll motion and reduces the non-stationary characteristics of the series.The energy entropy method reconstructs the mode components into high-frequency,medium-frequency,and low-frequency series to reduce model complexity.An improved black widow optimization algorithm trainer-based feedforward neural network with enhanced local optimal avoidance predicts the high-frequency component,enabling accurate tracking of abrupt signals.Additionally,the deterministic algorithm trainer-based neural network,characterized by rapid processing speed,predicts the remaining two mode components.Thus,real-time ship roll forecasting can be achieved through the reconstruction of mode component prediction results.The feasibility and effectiveness of the proposed hybrid prediction scheme for ship roll motion are demonstrated through the measured data of a full-scale ship trial.The proposed prediction scheme achieves real-time ship roll prediction with superior prediction accuracy.展开更多
To avoid the aerodynamic performance loss of airfoil at non-design state which often appears in single point design optimization, and to improve the adaptability to the uncertain factors in actual flight environment, ...To avoid the aerodynamic performance loss of airfoil at non-design state which often appears in single point design optimization, and to improve the adaptability to the uncertain factors in actual flight environment, a two-dimensional stochastic airfoil optimization design method based on neural networks is presented. To provide highly efficient and credible analysis, four BP neural networks are built as surrogate models to predict the airfoil aerodynamic coefficients and geometry parameter. These networks are combined with the probability density function obeying normal distribution and the genetic algorithm, thus forming an optimization design method. Using the method, for GA（W）-2 airfoil, a stochastic optimization is implemented in a two-dimensional flight area about Mach number and angle of attack. Compared with original airfoil and single point optimization design airfoil, results show that the two-dimensional stochastic method can improve the performance in a specific flight area, and increase the airfoil adaptability to the stochastic changes of multiple flight parameters.展开更多
The Open Flow implementations(SDNs) have been deployed increasingly on varieties of networks in research institutions as well as commercial institutions. To develop an Open Flow implementation, it is required to under...The Open Flow implementations(SDNs) have been deployed increasingly on varieties of networks in research institutions as well as commercial institutions. To develop an Open Flow implementation, it is required to understand the performance of the network. A few benchmark tools(e.g., Cbench and OFlops) can be used to measure the network performance, while these tools take considerable time to simulate traffic behaviors and generate the required results,therefore extending the development time. In this paper, we present an analytical model, which is based on stochastic network calculus theory, for evaluating the performance of switch to controller.The previous studies show that stochastic network calculus can provide realistic emulation of real network traffic behaviors. Our model is evaluated by using both simulation tool and realistic testbed.The results show the stochastic network calculus based analysis model can realistically measure the network performance of the end-to-end properties between controller and switch.展开更多
The stochastic resonance in paced time-delayed scale-free FitzHugh--Nagumo （FHN） neuronal networks is investigated. We show that an intermediate intensity of additive noise is able to optimally assist the pacemaker ...The stochastic resonance in paced time-delayed scale-free FitzHugh--Nagumo （FHN） neuronal networks is investigated. We show that an intermediate intensity of additive noise is able to optimally assist the pacemaker in imposing its rhythm on the whole ensemble. Furthermore, we reveal that appropriately tuned delays can induce stochastic multiresonances, appearing at every integer multiple of the pacemaker＇s oscillation period. We conclude that fine-tuned delay lengths and locally acting pacemakers are vital for ensuring optimal conditions for stochastic resonance on complex neuronal networks.展开更多
The problem of passivity analysis is investigated for uncertain stochastic neural networks with discrete interval and distributed time-varying delays.The parameter uncertainties are assumed to be norm bounded and the ...The problem of passivity analysis is investigated for uncertain stochastic neural networks with discrete interval and distributed time-varying delays.The parameter uncertainties are assumed to be norm bounded and the delay is assumed to be time-varying and belongs to a given interval,which means that the lower and upper bounds of interval time-varying delays are available.By constructing proper Lyapunov-Krasovskii functional and employing a combination of the free-weighting matrix method and stochastic analysis technique,new delay-dependent passivity conditions are derived in terms of linear matrix inequalities（LMIs）.Finally,numerical examples are given to show the less conservatism of the proposed conditions.展开更多
As a production quality index of hematite grinding process,particle size(PS)is hard to be measured in real time.To achieve the PS estimation,this paper proposes a novel data driven model of PS using stochastic configu...As a production quality index of hematite grinding process,particle size(PS)is hard to be measured in real time.To achieve the PS estimation,this paper proposes a novel data driven model of PS using stochastic configuration network(SCN)with robust technique,namely,robust SCN(RSCN).Firstly,this paper proves the universal approximation property of RSCN with weighted least squares technique.Secondly,three robust algorithms are presented by employing M-estimation with Huber loss function,M-estimation with interquartile range(IQR)and nonparametric kernel density estimation(NKDE)function respectively to set the penalty weight.Comparison experiments are first carried out based on the UCI standard data sets to verify the effectiveness of these methods,and then the data-driven PS model based on the robust algorithms are established and verified.Experimental results show that the RSCN has an excellent performance for the PS estimation.展开更多
Simultaneous use of heterogeneous radio access technologies to increase the performance of real-time,reliability and capacity is an inherent feature of satellite-5G integrated network(Sat5G).However,there is still a l...Simultaneous use of heterogeneous radio access technologies to increase the performance of real-time,reliability and capacity is an inherent feature of satellite-5G integrated network(Sat5G).However,there is still a lack of theoretical characterization of whether the network can satisfy the end-to-end transmission performance for latency-sensitive service.To this end,we build a tandem model considering the connection relationship between the various components in Sat5G network architecture,and give an end-to-end latency calculation function based on this model.By introducing stochastic network calculus,we derive the relationship between the end-to-end latency bound and the violation probability considering the traffic characteristics of multimedia.Numerical results demonstrate the impact of different burst states and different service rates on this relationship,which means the higher the burst of arrival traffic and the higher the average rate of arrival traffic,the greater the probability of end-to-end latency violation.The results will provide valuable guidelines for the traffic control and cache management in Sat5G network.展开更多
This paper studies the stochastic synchronization problem for time-varying complex dynamical networks. This model is totally different from some existing network models. Based on the Lyapunov stability theory, inequal...This paper studies the stochastic synchronization problem for time-varying complex dynamical networks. This model is totally different from some existing network models. Based on the Lyapunov stability theory, inequality techniques, and the properties of the Weiner process, some controllers and adaptive laws are designed to ensure achieving stochastic synchronization of a complex dynamical network model. A sufficient synchronization condition is given to ensure that the proposed network model is mean-square stable. Theoretical analysis and numerical simulation fully verify the main results.展开更多
This paper deals with the robust control problem for a class of uncertain nonlinear networked systems with stochastic communication delays via sliding mode conception （SMC）. A sequence of variables obeying Bernoulli...This paper deals with the robust control problem for a class of uncertain nonlinear networked systems with stochastic communication delays via sliding mode conception （SMC）. A sequence of variables obeying Bernoulli distribution are employed to model the randomly occurring communication delays which could be different for different state variables. A discrete switching function that is different from those in the existing literature is first proposed. Then, expressed as the feasibility of a linear matrix inequality （LMI） with an equality constraint, sufficient conditions are derived in order to ensure the globally mean-square asymptotic stability of the system dynamics on the sliding surface. A discrete-time SMC controller is then synthesized to guarantee the discrete-time sliding mode reaching condition with the specified sliding surface. Finally, a simulation example is given to show the effectiveness of the proposed method.展开更多
In this paper,an adaptive dynamic programming(ADP)strategy is investigated for discrete-time nonlinear systems with unknown nonlinear dynamics subject to input saturation.To save the communication resources between th...In this paper,an adaptive dynamic programming(ADP)strategy is investigated for discrete-time nonlinear systems with unknown nonlinear dynamics subject to input saturation.To save the communication resources between the controller and the actuators,stochastic communication protocols(SCPs)are adopted to schedule the control signal,and therefore the closed-loop system is essentially a protocol-induced switching system.A neural network(NN)-based identifier with a robust term is exploited for approximating the unknown nonlinear system,and a set of switch-based updating rules with an additional tunable parameter of NN weights are developed with the help of the gradient descent.By virtue of a novel Lyapunov function,a sufficient condition is proposed to achieve the stability of both system identification errors and the update dynamics of NN weights.Then,a value iterative ADP algorithm in an offline way is proposed to solve the optimal control of protocol-induced switching systems with saturation constraints,and the convergence is profoundly discussed in light of mathematical induction.Furthermore,an actor-critic NN scheme is developed to approximate the control law and the proposed performance index function in the framework of ADP,and the stability of the closed-loop system is analyzed in view of the Lyapunov theory.Finally,the numerical simulation results are presented to demonstrate the effectiveness of the proposed control scheme.展开更多
Looking at all the indeterminate factors as a whole and regarding activity durations as independent random variables, the traditional stochastic network planning models ignore the inevitable relationship and dependenc...Looking at all the indeterminate factors as a whole and regarding activity durations as independent random variables, the traditional stochastic network planning models ignore the inevitable relationship and dependence among activity durations when more than one activity is possibly affected by the same indeterminate factors. On this basis of analysis of indeterminate effect factors of durations, the effect factors-based stochastic network planning （EFBSNP） model is proposed, which emphasizes on the effects of not only logistic and organizational relationships, but also the dependent relationships, due to indeterminate factors among activity durations on the project period. By virtue of indeterminate factor analysis the model extracts and describes the quantitatively indeterminate effect factors, and then takes into account the indeterminate factors effect schedule by using the Monte Carlo simulation technique. The method is flexible enough to deal with effect factors and is coincident with practice. A software has been developed to simplify the model-based calculation, in VisualStudio.NET language. Finally, a case study is included to demonstrate the applicability of the proposed model and comparison is made with some advantages over the existing models.展开更多
With the aid of stochastic delayed-feedback differential equations, we derive an analytic expression for the power spectra of reacting molecules included in a generic biological network motif that is incorporated with...With the aid of stochastic delayed-feedback differential equations, we derive an analytic expression for the power spectra of reacting molecules included in a generic biological network motif that is incorporated with a feedback mechanism and time delays in gene regulation. We systematically analyse the effects of time delays, the feedback mechanism, and biological stochasticity on the power spectra. It has been clarified that the time delays together with the feedback mechanism can induce stochastic oscillations at the molecular level and invalidate the noise addition rule for a modular description of the noise propagator. Delay-induced stochastic resonance can be expected, which is related to the stability loss of the reaction systems and Hopf bifurcation occurring for solutions of the corresponding deterministic reaction equations. Through the analysis of the power spectrum, a new approach is proposed to estimate the oscillation period.展开更多
基金Supported by the National Natural Science Foundation of China(11971458,11471310)。
文摘In this paper,we propose a neural network approach to learn the parameters of a class of stochastic Lotka-Volterra systems.Approximations of the mean and covariance matrix of the observational variables are obtained from the Euler-Maruyama discretization of the underlying stochastic differential equations(SDEs),based on which the loss function is built.The stochastic gradient descent method is applied in the neural network training.Numerical experiments demonstrate the effectiveness of our method.
文摘Stochastic resonance can utilize the energy of noise to enhance weak frequency characteristic.This paper proposes an adaptive multi-stable stochastic resonance method assisted by the neural network(NN)and physics supervision(directly numerical simulation of the physical system).Different from traditional adaptive algorithm,the evaluation of the objective function(i.e.,fitness function)in iteration process of adaptive algorithm is through a trained neural network instead of the numerical simulation.It will bring a dramatically reduction in computation time.Considering predictive bias from the neural network,a secondary correction procedure is introduced to the reevaluate the top performers and then resort them in iteration process through physics supervision.Though it may increase the computing cost,the accuracy will be enhanced.Two examples are given to illustrate the proposed method.For a classical multi-stable stochastic resonance system,the results show that the proposed method not only amplifies weak signals effectively but also significantly reduces computing time.For the detection of weak signal from outer ring in bearings,by introducing a variable scale coefficient,the proposed method can also give a satisfactory result,and the characteristic frequency of the fault signal can be extracted correctly.
基金funded by Universiti Putra Malaysia under a Geran Putra Inisiatif(GPI)research grant with reference to GP-GPI/2023/9762100.
文摘This study proposes a novel time-synchronization protocol inspired by stochastic gradient algorithms.The clock model of each network node in this synchronizer is configured as a generic adaptive filter where different stochastic gradient algorithms can be adopted for adaptive clock frequency adjustments.The study analyzes the pairwise synchronization behavior of the protocol and proves the generalized convergence of the synchronization error and clock frequency.A novel closed-form expression is also derived for a generalized asymptotic error variance steady state.Steady and convergence analyses are then presented for the synchronization,with frequency adaptations done using least mean square(LMS),the Newton search,the gradient descent(GraDes),the normalized LMS(N-LMS),and the Sign-Data LMS algorithms.Results obtained from real-time experiments showed a better performance of our protocols as compared to the Average Proportional-Integral Synchronization Protocol(AvgPISync)regarding the impact of quantization error on synchronization accuracy,precision,and convergence time.This generalized approach to time synchronization allows flexibility in selecting a suitable protocol for different wireless sensor network applications.
基金supported by the National Natural Science Foundation of China under Grant Nos.U21A20464,62066005Innovation Project of Guangxi Graduate Education under Grant No.YCSW2024313.
文摘Wireless sensor network deployment optimization is a classic NP-hard problem and a popular topic in academic research.However,the current research on wireless sensor network deployment problems uses overly simplistic models,and there is a significant gap between the research results and actual wireless sensor networks.Some scholars have now modeled data fusion networks to make them more suitable for practical applications.This paper will explore the deployment problem of a stochastic data fusion wireless sensor network(SDFWSN),a model that reflects the randomness of environmental monitoring and uses data fusion techniques widely used in actual sensor networks for information collection.The deployment problem of SDFWSN is modeled as a multi-objective optimization problem.The network life cycle,spatiotemporal coverage,detection rate,and false alarm rate of SDFWSN are used as optimization objectives to optimize the deployment of network nodes.This paper proposes an enhanced multi-objective mongoose optimization algorithm(EMODMOA)to solve the deployment problem of SDFWSN.First,to overcome the shortcomings of the DMOA algorithm,such as its low convergence and tendency to get stuck in a local optimum,an encircling and hunting strategy is introduced into the original algorithm to propose the EDMOA algorithm.The EDMOA algorithm is designed as the EMODMOA algorithm by selecting reference points using the K-Nearest Neighbor(KNN)algorithm.To verify the effectiveness of the proposed algorithm,the EMODMOA algorithm was tested at CEC 2020 and achieved good results.In the SDFWSN deployment problem,the algorithm was compared with the Non-dominated Sorting Genetic Algorithm II(NSGAII),Multiple Objective Particle Swarm Optimization(MOPSO),Multi-Objective Evolutionary Algorithm based on Decomposition(MOEA/D),and Multi-Objective Grey Wolf Optimizer(MOGWO).By comparing and analyzing the performance evaluation metrics and optimization results of the objective functions of the multi-objective algorithms,the algorithm outperforms the other algorithms in the SDFWSN deployment results.To better demonstrate the superiority of the algorithm,simulations of diverse test cases were also performed,and good results were obtained.
基金supported in part by the National Key Research and Development Program of China(2022YFB3304900)the Science and Technology Innovation Program of Hunan Province(2022RC1089)+1 种基金the Central South University Innovation Driven Research Programme(2023CXQD040)the Fundamental Research Funds for the Central Universities of Central South University(2025ZZTS0213).
文摘Complex industrial processes present typical uncertainty due to fluctuations in the composition of raw materials and frequently changing operating conditions.This poses three challenges for precise fault diagnosis,including random noise interference,less distinguishability between multi-class faults,and the new fault emerging.To address these issues,this study formulates fault diagnosis in uncertain industrial processes as a multilevel refined fault diagnosis problem.A hierarchical stochastic network approach is proposed to refine fault diagnosis of multiclass faults.This method considers the augmentation of fault categories as naturally following a hierarchical structure.At each hierarchical stage,stochastic network methods are designed according to the sources of uncertainty.For fault feature extraction,a doubly stochastic attention-based variational graph autoencoder is introduced to suppress noise during the messagepassing process,ensuring the extraction of high-quality fault features and providing the provision of differentiated information.Subsequently,multiple stochastic configuration networks are deployed to realize multi-level fault diagnosis from coarse to fine granularity via a hierarchical structure rather than treating all faults equally.This approach effectively enhances the precision of multi-class fault diagnosis and ensures its robust generalization capability.Finally,the feasibility and effectiveness of the proposed method are validated using two industrial processes.The results demonstrate that the proposed method can effectively suppress the random noise interference and adapt to the emergence of small samples and imbalanced extreme fault-type data,achieving a satisfactory fault diagnosis performance.
基金supported by the National Natural Science Foundation of China(72101025,72271049),the Interdisciplinary Research Project for Young Teachers of USTB(Fundamental Research Funds for the Central Universities,FRF-IDRY-24-024)the Hebei Natural Science Foundation(F2023501011)+1 种基金the Fundamental Research Funds for the Central Universities(FRF-TP-20-073A1)the R&D Program of Beijing Municipal Education Commission(KM202411232015).
文摘This paper proposes a reliability evaluation model for a multi-dimensional network system,which has potential to be applied to the internet of things or other practical networks.A multi-dimensional network system with one source element and multiple sink elements is considered first.Each element can con-nect with other elements within a stochastic connection ranges.The system is regarded as successful as long as the source ele-ment remains connected with all sink elements.An importance measure is proposed to evaluate the performance of non-source elements.Furthermore,to calculate the system reliability and the element importance measure,a multi-valued decision diagram based approach is structured and its complexity is analyzed.Finally,a numerical example about the signal transfer station system is illustrated to analyze the system reliability and the ele-ment importance measure.
基金Supported by the National Natural Science Foundation of China(12261098,11861072)。
文摘In this paper,we use a direct method to study the almost periodic dynamics of an octonion-valued stochastic shunting inhibitory cellular neural network with variable delays.By using the fixed point method and inequality technique,the existence,uniqueness and stability of almost periodic solutions in the sense of distribution of the neural network under consideration are obtained.Our results are brand new.
基金supported by the National Natural Science Foundation of China(62373017).
文摘To improve the generalization performance and prediction accuracy of the stochastic configuration network(SCN)model,a novel SCN modeling method is proposed.First,the first-and second-order directional derivatives of the hidden layer output matrix are calculated.The key factors extracted from the directional derivatives are linearly added to the original hidden layer output matrix to formulate a new hidden layer output matrix.Second,a spatial angle adaptive supervisory mechanism is established to improve the quality of the parameter configuration of the hidden layer nodes.The experimental results show that the proposed method improves the generalization performance and prediction accuracy.This work is a beneficial exploration of the standard SCN algorithm.
基金supported by the National Natural Science Foundation of China(Grant Nos.52231014 and 52271361)the Natural Science Foundation of Guangdong Province of China(Grant No.2023A1515010684).
文摘Enhancing the accuracy of real-time ship roll prediction is crucial for maritime safety and operational efficiency.To address the challenge of accurately predicting the ship roll status with nonlinear time-varying dynamic characteristics,a real-time ship roll prediction scheme is proposed on the basis of a data preprocessing strategy and a novel stochastic trainer-based feedforward neural network.The sliding data window serves as a ship time-varying dynamic observer to enhance model prediction stability.The variational mode decomposition method extracts effective information on ship roll motion and reduces the non-stationary characteristics of the series.The energy entropy method reconstructs the mode components into high-frequency,medium-frequency,and low-frequency series to reduce model complexity.An improved black widow optimization algorithm trainer-based feedforward neural network with enhanced local optimal avoidance predicts the high-frequency component,enabling accurate tracking of abrupt signals.Additionally,the deterministic algorithm trainer-based neural network,characterized by rapid processing speed,predicts the remaining two mode components.Thus,real-time ship roll forecasting can be achieved through the reconstruction of mode component prediction results.The feasibility and effectiveness of the proposed hybrid prediction scheme for ship roll motion are demonstrated through the measured data of a full-scale ship trial.The proposed prediction scheme achieves real-time ship roll prediction with superior prediction accuracy.
文摘To avoid the aerodynamic performance loss of airfoil at non-design state which often appears in single point design optimization, and to improve the adaptability to the uncertain factors in actual flight environment, a two-dimensional stochastic airfoil optimization design method based on neural networks is presented. To provide highly efficient and credible analysis, four BP neural networks are built as surrogate models to predict the airfoil aerodynamic coefficients and geometry parameter. These networks are combined with the probability density function obeying normal distribution and the genetic algorithm, thus forming an optimization design method. Using the method, for GA（W）-2 airfoil, a stochastic optimization is implemented in a two-dimensional flight area about Mach number and angle of attack. Compared with original airfoil and single point optimization design airfoil, results show that the two-dimensional stochastic method can improve the performance in a specific flight area, and increase the airfoil adaptability to the stochastic changes of multiple flight parameters.
基金supported by the National Science and Technology Support Program (2014BAH24F01)the National Basic Research Program of China(2012CB3 15903)+3 种基金the Program for Key Science and Technology Innovation Team of Zhejiang Province (2011R50010-21,2013TD20)863 Program of China(2015AA015602,2015AA016103)the National Natural Science Foundation of China (61379118)the Fundamental Research Funds for the Central Universities
文摘The Open Flow implementations(SDNs) have been deployed increasingly on varieties of networks in research institutions as well as commercial institutions. To develop an Open Flow implementation, it is required to understand the performance of the network. A few benchmark tools(e.g., Cbench and OFlops) can be used to measure the network performance, while these tools take considerable time to simulate traffic behaviors and generate the required results,therefore extending the development time. In this paper, we present an analytical model, which is based on stochastic network calculus theory, for evaluating the performance of switch to controller.The previous studies show that stochastic network calculus can provide realistic emulation of real network traffic behaviors. Our model is evaluated by using both simulation tool and realistic testbed.The results show the stochastic network calculus based analysis model can realistically measure the network performance of the end-to-end properties between controller and switch.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 10672140,10972001 and 10832006)Matjaz Perc individually acknowledges the Support from the Slovenian Research Agency (Grant Nos. Z1-9629 and Z1-2032-2547)
文摘The stochastic resonance in paced time-delayed scale-free FitzHugh--Nagumo （FHN） neuronal networks is investigated. We show that an intermediate intensity of additive noise is able to optimally assist the pacemaker in imposing its rhythm on the whole ensemble. Furthermore, we reveal that appropriately tuned delays can induce stochastic multiresonances, appearing at every integer multiple of the pacemaker＇s oscillation period. We conclude that fine-tuned delay lengths and locally acting pacemakers are vital for ensuring optimal conditions for stochastic resonance on complex neuronal networks.
基金supported by Department of Science and Technology,New Delhi,India(SR/S4/MS:485/07)
文摘The problem of passivity analysis is investigated for uncertain stochastic neural networks with discrete interval and distributed time-varying delays.The parameter uncertainties are assumed to be norm bounded and the delay is assumed to be time-varying and belongs to a given interval,which means that the lower and upper bounds of interval time-varying delays are available.By constructing proper Lyapunov-Krasovskii functional and employing a combination of the free-weighting matrix method and stochastic analysis technique,new delay-dependent passivity conditions are derived in terms of linear matrix inequalities（LMIs）.Finally,numerical examples are given to show the less conservatism of the proposed conditions.
基金Projects(61603393,61741318)supported in part by the National Natural Science Foundation of ChinaProject(BK20160275)supported by the Natural Science Foundation of Jiangsu Province,China+1 种基金Project(2015M581885)supported by the Postdoctoral Science Foundation of ChinaProject(PAL-N201706)supported by the Open Project Foundation of State Key Laboratory of Synthetical Automation for Process Industries of Northeastern University,China
文摘As a production quality index of hematite grinding process,particle size(PS)is hard to be measured in real time.To achieve the PS estimation,this paper proposes a novel data driven model of PS using stochastic configuration network(SCN)with robust technique,namely,robust SCN(RSCN).Firstly,this paper proves the universal approximation property of RSCN with weighted least squares technique.Secondly,three robust algorithms are presented by employing M-estimation with Huber loss function,M-estimation with interquartile range(IQR)and nonparametric kernel density estimation(NKDE)function respectively to set the penalty weight.Comparison experiments are first carried out based on the UCI standard data sets to verify the effectiveness of these methods,and then the data-driven PS model based on the robust algorithms are established and verified.Experimental results show that the RSCN has an excellent performance for the PS estimation.
基金This work was supported by the National Natural Science Foundation of China under Grants 61801073,61722105,61931004the Natural Science Foundation of Liaoning Province under Grant 20170540034.
文摘Simultaneous use of heterogeneous radio access technologies to increase the performance of real-time,reliability and capacity is an inherent feature of satellite-5G integrated network(Sat5G).However,there is still a lack of theoretical characterization of whether the network can satisfy the end-to-end transmission performance for latency-sensitive service.To this end,we build a tandem model considering the connection relationship between the various components in Sat5G network architecture,and give an end-to-end latency calculation function based on this model.By introducing stochastic network calculus,we derive the relationship between the end-to-end latency bound and the violation probability considering the traffic characteristics of multimedia.Numerical results demonstrate the impact of different burst states and different service rates on this relationship,which means the higher the burst of arrival traffic and the higher the average rate of arrival traffic,the greater the probability of end-to-end latency violation.The results will provide valuable guidelines for the traffic control and cache management in Sat5G network.
基金supported by the National Natural Science Foundation of China (Grant No.60974139)the Fundamental Research Funds for the Central Universities (Grant No.72103676)
文摘This paper studies the stochastic synchronization problem for time-varying complex dynamical networks. This model is totally different from some existing network models. Based on the Lyapunov stability theory, inequality techniques, and the properties of the Weiner process, some controllers and adaptive laws are designed to ensure achieving stochastic synchronization of a complex dynamical network model. A sufficient synchronization condition is given to ensure that the proposed network model is mean-square stable. Theoretical analysis and numerical simulation fully verify the main results.
基金supported by the Engineering and Physical Sciences Research Council(EPSRC)of the UK(No.GR/S27658/01)the Royal Society of the UK and the Alexander von Humboldt Foundation of Germany
文摘This paper deals with the robust control problem for a class of uncertain nonlinear networked systems with stochastic communication delays via sliding mode conception （SMC）. A sequence of variables obeying Bernoulli distribution are employed to model the randomly occurring communication delays which could be different for different state variables. A discrete switching function that is different from those in the existing literature is first proposed. Then, expressed as the feasibility of a linear matrix inequality （LMI） with an equality constraint, sufficient conditions are derived in order to ensure the globally mean-square asymptotic stability of the system dynamics on the sliding surface. A discrete-time SMC controller is then synthesized to guarantee the discrete-time sliding mode reaching condition with the specified sliding surface. Finally, a simulation example is given to show the effectiveness of the proposed method.
基金supported in part by the Australian Research Council Discovery Early Career Researcher Award(DE200101128)Australian Research Council(DP190101557)。
文摘In this paper,an adaptive dynamic programming(ADP)strategy is investigated for discrete-time nonlinear systems with unknown nonlinear dynamics subject to input saturation.To save the communication resources between the controller and the actuators,stochastic communication protocols(SCPs)are adopted to schedule the control signal,and therefore the closed-loop system is essentially a protocol-induced switching system.A neural network(NN)-based identifier with a robust term is exploited for approximating the unknown nonlinear system,and a set of switch-based updating rules with an additional tunable parameter of NN weights are developed with the help of the gradient descent.By virtue of a novel Lyapunov function,a sufficient condition is proposed to achieve the stability of both system identification errors and the update dynamics of NN weights.Then,a value iterative ADP algorithm in an offline way is proposed to solve the optimal control of protocol-induced switching systems with saturation constraints,and the convergence is profoundly discussed in light of mathematical induction.Furthermore,an actor-critic NN scheme is developed to approximate the control law and the proposed performance index function in the framework of ADP,and the stability of the closed-loop system is analyzed in view of the Lyapunov theory.Finally,the numerical simulation results are presented to demonstrate the effectiveness of the proposed control scheme.
文摘Looking at all the indeterminate factors as a whole and regarding activity durations as independent random variables, the traditional stochastic network planning models ignore the inevitable relationship and dependence among activity durations when more than one activity is possibly affected by the same indeterminate factors. On this basis of analysis of indeterminate effect factors of durations, the effect factors-based stochastic network planning （EFBSNP） model is proposed, which emphasizes on the effects of not only logistic and organizational relationships, but also the dependent relationships, due to indeterminate factors among activity durations on the project period. By virtue of indeterminate factor analysis the model extracts and describes the quantitatively indeterminate effect factors, and then takes into account the indeterminate factors effect schedule by using the Monte Carlo simulation technique. The method is flexible enough to deal with effect factors and is coincident with practice. A software has been developed to simplify the model-based calculation, in VisualStudio.NET language. Finally, a case study is included to demonstrate the applicability of the proposed model and comparison is made with some advantages over the existing models.
基金Project supported by the National Natural Science Foundation of China (Grant No. 10975019)the Foundation of the Ministry of Personnel of China for Returned Scholars (Grant No. MOP2006138)the Fundamental Research Funds for the Central Universities
文摘With the aid of stochastic delayed-feedback differential equations, we derive an analytic expression for the power spectra of reacting molecules included in a generic biological network motif that is incorporated with a feedback mechanism and time delays in gene regulation. We systematically analyse the effects of time delays, the feedback mechanism, and biological stochasticity on the power spectra. It has been clarified that the time delays together with the feedback mechanism can induce stochastic oscillations at the molecular level and invalidate the noise addition rule for a modular description of the noise propagator. Delay-induced stochastic resonance can be expected, which is related to the stability loss of the reaction systems and Hopf bifurcation occurring for solutions of the corresponding deterministic reaction equations. Through the analysis of the power spectrum, a new approach is proposed to estimate the oscillation period.
