The information exchange among satellites is crucial for the implementation of cluster satellite cooperative missions.However,achieving fast perception,rapid networking,and highprecision time synchronization among nod...The information exchange among satellites is crucial for the implementation of cluster satellite cooperative missions.However,achieving fast perception,rapid networking,and highprecision time synchronization among nodes without the support of the Global Navigation Satellite System(GNSS)and other prior information remains a formidable challenge to real-time wireless networks design.Therefore,a self-organizing network methodology based on multi-agent negotiation is proposed,which autonomously determines the master node through collaborative negotiation and competitive elections.On this basis,a real-time network protocol design is carried out and a high-precision time synchronization method with motion compensation is proposed.Simulation results demonstrate that the proposed method enables rapid networking with the capability of selfdiscovery,self-organization,and self-healing.For a cluster of 8 satellites,the networking time and the reorganization time are less than 4 s.The time synchronization accuracy exceeds 10-10s with motion compensation,demonstrating excellent real-time performance and stability.The research presented in this paper provides a valuable reference for the design and application of spacebased self-organizing networks for satellite cluster.展开更多
With the rapid development of the internet,the dissemination of public opinion in online social networks has become increasingly complex.Existing dissemination models rarely consider group phenomena and the simultaneo...With the rapid development of the internet,the dissemination of public opinion in online social networks has become increasingly complex.Existing dissemination models rarely consider group phenomena and the simultaneous spread of competing public opinion information in online social networks.This paper introduces the UHNPR information dissemination model to study the dynamic spread and interaction of positive and negative public opinion information in hypernetworks.To improve the accuracy of modeling of information dissemination,we revise the traditional assumptions of constant propagation and decay rates by redefining these rates based on factors that influence the spread of public opinion information.Subsequently,we validate the effectiveness of the UHNPR model using numerical simulations and analyze the impact of factors such as authority effect,user intimacy,information content and information timeliness on the spread of public opinion,providing corresponding suggestions for public opinion control.Our research results demonstrate that this model outperforms the SIR,SEIR and SEIDR models in describing public opinion propagation in real social networks.Compared with complex networks,information spreads faster and more extensively in hypernetworks.展开更多
This article presents an adaptive fault-tolerant tracking control strategy for unknown affine nonlinear systems subject to actuator faults and external disturbances.To address the hyperparameter initialization challen...This article presents an adaptive fault-tolerant tracking control strategy for unknown affine nonlinear systems subject to actuator faults and external disturbances.To address the hyperparameter initialization challenges inherent in conventional neural network training,an improved self-organizing radial basis function neural network(SRBFNN)with an input-dependent variable structure is developed.Furthermore,a novel selforganizing RBFNN-based observer is introduced to estimate system states across all dimensions.Leveraging the reconstructed states,the proposed adaptive controller effectively compensates for all uncertainties,including estimation errors in the observer,ensuring accurate state tracking with reduced control effort.The uniform ultimate boundedness of all closed-loop signals and tracking errors is rigorously established via Lyapunov stability analysis.Finally,simulations on two different nonlinear systems comprehensively validate the effectiveness and superiority of the proposed control approach.展开更多
Due to the widespread use of the Internet,customer information is vulnerable to computer systems attack,which brings urgent need for the intrusion detection technology.Recently,network intrusion detection has been one...Due to the widespread use of the Internet,customer information is vulnerable to computer systems attack,which brings urgent need for the intrusion detection technology.Recently,network intrusion detection has been one of the most important technologies in network security detection.The accuracy of network intrusion detection has reached higher accuracy so far.However,these methods have very low efficiency in network intrusion detection,even the most popular SOM neural network method.In this paper,an efficient and fast network intrusion detection method was proposed.Firstly,the fundamental of the two different methods are introduced respectively.Then,the selforganizing feature map neural network based on K-means clustering(KSOM)algorithms was presented to improve the efficiency of network intrusion detection.Finally,the NSLKDD is used as network intrusion data set to demonstrate that the KSOM method can significantly reduce the number of clustering iteration than SOM method without substantially affecting the clustering results and the accuracy is much higher than Kmeans method.The Experimental results show that our method can relatively improve the accuracy of network intrusion and significantly reduce the number of clustering iteration.展开更多
The principle that ＇the brand effect is attractive＇ underlies the preferential attachment. Here we show that the brand effect is just one dimension of attractiveness. Another dimension is competitiveness. We firstly...The principle that ＇the brand effect is attractive＇ underlies the preferential attachment. Here we show that the brand effect is just one dimension of attractiveness. Another dimension is competitiveness. We firstly introduce a general frame- work that allows us to investigate the competitive aspect of real networks, instead of simply preferring popular nodes. Our model accurately describes the evolution of social and technological networks. The phenomenon that more competitive nodes become richer can help us to understand the evolution of many competitive systems in nature and society. In general, the paper provides an explicit analytical expression of degree distributions of the network. In particular, the model yields a nontrivial time evolution of nodes＇ properties and the scale-free behavior with exponents depending on the microscopic parameters characterizing the competition rules. Secondly, through theoretical analyses and numerical simulations, we reveal that our model has not only the universality for the homogeneous weighted network, but also the character for the heterogeneous weighted network. Thirdly, we also develop a model based on the profit-driven mechanism. It can better describe the observed phenomenon in enterprise cooperation networks. We show that the standard preferential attachment, the growing random graph, the initial attractiveness model, the fitness model, and weighted networks can all be seen as degenerate cases of our model.展开更多
Due to rapid urbanization, waterlogging induced by torrential rainfall has become a global concern and a potential risk affecting urban habitant's safety. Widespread waterlogging disasters haveoccurred almost annu...Due to rapid urbanization, waterlogging induced by torrential rainfall has become a global concern and a potential risk affecting urban habitant's safety. Widespread waterlogging disasters haveoccurred almost annuallyinthe urban area of Beijing, the capital of China. Based on a selforganizing map(SOM) artificial neural network(ANN), a graded waterlogging risk assessment was conducted on 56 low-lying points in Beijing, China. Social risk factors, such as Gross domestic product(GDP), population density, and traffic congestion, were utilized as input datasets in this study. The results indicate that SOM-ANNis suitable for automatically and quantitatively assessing risks associated with waterlogging. The greatest advantage of SOM-ANN in the assessment of waterlogging risk is that a priori knowledge about classification categories and assessment indicator weights is not needed. As a result, SOM-ANN can effectively overcome interference from subjective factors,producing classification results that are more objective and accurate. In this paper, the risk level of waterlogging in Beijing was divided into five grades. The points that were assigned risk grades of IV or Vwere located mainly in the districts of Chaoyang, Haidian, Xicheng, and Dongcheng.展开更多
Fiber reinforced polymers (FRPs), unlike steel, are corrosion-resistant and therefore are of interest;however, their use is hindered because their brittle shear is formulated in most specifications using limited data ...Fiber reinforced polymers (FRPs), unlike steel, are corrosion-resistant and therefore are of interest;however, their use is hindered because their brittle shear is formulated in most specifications using limited data available at the time. We aimed to predict the shear strength of concrete beams reinforced with FRP bars and without stirrups by compiling a relatively large database of 198 previously published test results (available in appendix). To model shear strength, an artificial neural network was trained by an ensemble of Levenberg-Marquardt and imperialist competitive algorithms. The results suggested superior accuracy of model compared to equations available in specifications and literature.展开更多
Internet of things and network densification bring significant challenges to uplink management.Only depending on optimization algorithm enhancements is not enough for uplink transmission.To control intercell interfere...Internet of things and network densification bring significant challenges to uplink management.Only depending on optimization algorithm enhancements is not enough for uplink transmission.To control intercell interference,Fractional Uplink Power Control(FUPC)should be optimized from network-wide perspective,which has to find a better traffic distribution model.Conventionally,traffic distribution is geographic-based,and ineffective due to tricky locating efforts.This paper proposes a novel uplink power management framework for Self-Organizing Networks(SON),which firstly builds up pathloss-based traffic distribution model and then makes the decision of FUPC based on the model.PathLoss-based Traffic Distribution(PLTD)aggregates traffic based on the propagation condition of traffic that is defined as the pathloss between the position generating the traffic and surrounding cells.Simulations show that the improvement in optimization efficiency of FUPC with PLTD can be up to 40%compared to conventional GeoGraphic-based Traffic Distribution(GGTD).展开更多
This paper studies the global exponential stability of competitive neural networks with different time scales and time-varying delays. By using the method of the proper Lyapunov functions and inequality technique, som...This paper studies the global exponential stability of competitive neural networks with different time scales and time-varying delays. By using the method of the proper Lyapunov functions and inequality technique, some sufficient conditions are presented for global exponential stability of delay competitive neural networks with different time scales. These conditions obtained have important leading significance in the designs and applications of global exponential stability for competitive neural networks. Finally, an example with its simulation is provided to demonstrate the usefulness of the proposed criteria.展开更多
The virtuality and openness of online social platforms make networks a hotbed for the rapid propagation of various rumors.In order to block the outbreak of rumor,one of the most effective containment measures is sprea...The virtuality and openness of online social platforms make networks a hotbed for the rapid propagation of various rumors.In order to block the outbreak of rumor,one of the most effective containment measures is spreading positive information to counterbalance the diffusion of rumor.The spreading mechanism of rumors and effective suppression strategies are significant and challenging research issues.Firstly,in order to simulate the dissemination of multiple types of information,we propose a competitive linear threshold model with state transition(CLTST)to describe the spreading process of rumor and anti-rumor in the same network.Subsequently,we put forward a community-based rumor blocking(CRB)algorithm based on influence maximization theory in social networks.Its crucial step is to identify a set of influential seeds that propagate anti-rumor information to other nodes,which includes community detection,selection of candidate anti-rumor seeds and generation of anti-rumor seed set.Under the CLTST model,the CRB algorithm has been compared with six state-of-the-art algorithms on nine online social networks to verify the performance.Experimental results show that the proposed model can better reflect the process of rumor propagation,and review the propagation mechanism of rumor and anti-rumor in online social networks.Moreover,the proposed CRB algorithm has better performance in weakening the rumor dissemination ability,which can select anti-rumor seeds in networks more accurately and achieve better performance in influence spread,sensitivity analysis,seeds distribution and running time.展开更多
Traveling salesman problem(TSP)is a classic non-deterministic polynomial-hard optimization prob-lem.Based on the characteristics of self-organizing mapping(SOM)network,this paper proposes an improved SOM network from ...Traveling salesman problem(TSP)is a classic non-deterministic polynomial-hard optimization prob-lem.Based on the characteristics of self-organizing mapping(SOM)network,this paper proposes an improved SOM network from the perspectives of network update strategy,initialization method,and parameter selection.This paper compares the performance of the proposed algorithms with the performance of existing SOM network algorithms on the TSP and compares them with several heuristic algorithms.Simulations show that compared with existing SOM networks,the improved SOM network proposed in this paper improves the convergence rate and algorithm accuracy.Compared with iterated local search and heuristic algorithms,the improved SOM net-work algorithms proposed in this paper have the advantage of fast calculation speed on medium-scale TSP.展开更多
On the basis of asymptotic theory of Gersho, the isodistortion principle of vector clustering was discussed and a kind of competitive and selective learning method (CSL) which may avoid local optimization and have exc...On the basis of asymptotic theory of Gersho, the isodistortion principle of vector clustering was discussed and a kind of competitive and selective learning method (CSL) which may avoid local optimization and have excellent result in application to clusters of HMM model was also proposed. In combining the parallel, self organizational hierarchical neural networks (PSHNN) to reclassify the scores of every form output by HMM, the CSL speech recognition rate is obviously elevated.展开更多
Introduce a method of generation of new units within a cluster and aalgorithm of generating new clusters. The model automatically builds up its dynamically growinginternal representation structure during the learning ...Introduce a method of generation of new units within a cluster and aalgorithm of generating new clusters. The model automatically builds up its dynamically growinginternal representation structure during the learning process. Comparing model with other typicalclassification algorithm such as the Kohonen's self-organizing map, the model realizes a multilevelclassification of the input pattern with an optional accuracy and gives a strong support possibilityfor the parallel computational main processor. The idea is suitable for the high-level storage ofcomplex datas structures for object recognition.展开更多
The shortcomings of traditional methods to find the shortest path are revealed, and a strategy of finding the self- organizing shortest path based on thermal flux diffusion on complex networks is presented. In our met...The shortcomings of traditional methods to find the shortest path are revealed, and a strategy of finding the self- organizing shortest path based on thermal flux diffusion on complex networks is presented. In our method, the shortest paths between the source node and the other nodes are found to be self-organized by comparing node temperatures. The computation complexity of the method scales linearly with the number of edges on underlying networks. The effects of the method on several networks, including a regular network proposed by Ravasz and Barabasi which is called the RB network, a real network, a random network proposed by Ravasz and Barabasi which is called the ER network and a scale-free network, are also demonstrated. Analytic and simulation results show that the method has a higher accuracy and lower computational complexity than the conventional methods.展开更多
The typical characteristic of the topology of Bayesian networks (BNs) is the interdependence among different nodes (variables), which makes it impossible to optimize one variable independently of others, and the learn...The typical characteristic of the topology of Bayesian networks (BNs) is the interdependence among different nodes (variables), which makes it impossible to optimize one variable independently of others, and the learning of BNs structures by general genetic algorithms is liable to converge to local extremum. To resolve efficiently this problem, a self-organizing genetic algorithm (SGA) based method for constructing BNs from databases is presented. This method makes use of a self-organizing mechanism to develop a genetic algorithm that extended the crossover operator from one to two, providing mutual competition between them, even adjusting the numbers of parents in recombination (crossover/recomposition) schemes. With the K2 algorithm, this method also optimizes the genetic operators, and utilizes adequately the domain knowledge. As a result, with this method it is able to find a global optimum of the topology of BNs, avoiding premature convergence to local extremum. The experimental results proved to be and the convergence of the SGA was discussed.展开更多
Haze-fog,which is an atmospheric aerosol caused by natural or man-made factors,seriously affects the physical and mental health of human beings.PM2.5(a particulate matter whose diameter is smaller than or equal to 2.5...Haze-fog,which is an atmospheric aerosol caused by natural or man-made factors,seriously affects the physical and mental health of human beings.PM2.5(a particulate matter whose diameter is smaller than or equal to 2.5 microns)is the chief culprit causing aerosol.To forecast the condition of PM2.5,this paper adopts the related the meteorological data and air pollutes data to predict the concentration of PM2.5.Since the meteorological data and air pollutes data are typical time series data,it is reasonable to adopt a machine learning method called Single Hidden-Layer Long Short-Term Memory Neural Network(SSHL-LSTMNN)containing memory capability to implement the prediction.However,the number of neurons in the hidden layer is difficult to decide unless manual testing is operated.In order to decide the best structure of the neural network and improve the accuracy of prediction,this paper employs a self-organizing algorithm,which uses Information Processing Capability(IPC)to adjust the number of the hidden neurons automatically during a learning phase.In a word,to predict PM2.5 concentration accurately,this paper proposes the SSHL-LSTMNN to predict PM2.5 concentration.In the experiment,not only the hourly precise prediction but also the daily longer-term prediction is taken into account.At last,the experimental results reflect that SSHL-LSTMNN performs the best.展开更多
Because interval value is quite natural in clustering, an interval-valued fuzzy competitive neural network is proposed. Firstly, this paper proposes several definitions of distance relating to interval number. And the...Because interval value is quite natural in clustering, an interval-valued fuzzy competitive neural network is proposed. Firstly, this paper proposes several definitions of distance relating to interval number. And then, it indicates the method of preprocessing input data, the structure of the network and the learning algorithm of the interval-valued fuzzy competitive neural network. This paper also analyses the principle of the learning algorithm. At last, an experiment is used to test the validity of the network.展开更多
The territorial consideration is enclosed in the development of network economy and e-Business, and there is no departure of the cultivation of e-Business from the establishment of territorial competitiveness. The tra...The territorial consideration is enclosed in the development of network economy and e-Business, and there is no departure of the cultivation of e-Business from the establishment of territorial competitiveness. The traditional theory of the territorial competitiveness has come across a serious impact from the Internet. So the development of Intemet infrastructures and institutional environment would be something new to the theory of territorial competitiveness in the Internet era.展开更多
Combining with the characters of the practicing qualification personnel in construction market,evaluation method based on the self-organizing neural network is brought out to analyze the credit classification of the p...Combining with the characters of the practicing qualification personnel in construction market,evaluation method based on the self-organizing neural network is brought out to analyze the credit classification of the practicing qualification personnel. And the impact factors on the credit classification of the practicing qualification personnel,such as the number of neurons,the training steps,the dimension of neurons and the field of winning neurons are studied. Then a self-organizing competitive neural network is built. At last,a case study is conducted by taking practicing qualification personnel as an example. The research result reveals that the method can efficiently evaluate the credit of the practicing qualification personnel;thus,it could provide scientific advice to the construction enterprise to prevent relevant discreditable behaviors of some practicing qualification personnel.展开更多
The artificial neural networks (ANNs), among different soft computing methodologies are widely used to meet the challenges thrown by the main objectives of data mining classification techniques, due to their robust, p...The artificial neural networks (ANNs), among different soft computing methodologies are widely used to meet the challenges thrown by the main objectives of data mining classification techniques, due to their robust, powerful, distributed, fault tolerant computing and capability to learn in a data-rich environment. ANNs has been used in several fields, showing high performance as classifiers. The problem of dealing with non numerical data is one major obstacle prevents using them with various data sets and several domains. Another problem is their complex structure and how hands to interprets. Self-Organizing Map (SOM) is type of neural systems that can be easily interpreted, but still can’t be used with non numerical data directly. This paper presents an enhanced SOM structure to cope with non numerical data. It used DNA sequences as the training dataset. Results show very good performance compared to other classifiers. For better evaluation both micro-array structure and their sequential representation as proteins were targeted as dataset accuracy is measured accordingly.展开更多
基金supported by the National Natural Science Foundation of China(No.62401597)the Natural Science Foundation of Hunan Province,China(No.2024JJ6469)the Scientific Research Project of National University of Defense Technology,China(No.ZK22-02)。
文摘The information exchange among satellites is crucial for the implementation of cluster satellite cooperative missions.However,achieving fast perception,rapid networking,and highprecision time synchronization among nodes without the support of the Global Navigation Satellite System(GNSS)and other prior information remains a formidable challenge to real-time wireless networks design.Therefore,a self-organizing network methodology based on multi-agent negotiation is proposed,which autonomously determines the master node through collaborative negotiation and competitive elections.On this basis,a real-time network protocol design is carried out and a high-precision time synchronization method with motion compensation is proposed.Simulation results demonstrate that the proposed method enables rapid networking with the capability of selfdiscovery,self-organization,and self-healing.For a cluster of 8 satellites,the networking time and the reorganization time are less than 4 s.The time synchronization accuracy exceeds 10-10s with motion compensation,demonstrating excellent real-time performance and stability.The research presented in this paper provides a valuable reference for the design and application of spacebased self-organizing networks for satellite cluster.
基金supported by Yunnan High-tech Industry Development Project(Grant No.201606)Yunnan Provincial Major Science and Technology Special Plan Projects(Grant Nos.202103AA080015 and 202002AD080001-5)+1 种基金Yunnan Basic Research Project(Grant No.202001AS070014)Talents and Platform Program of Science and Technology of Yunnan(Grant No.202105AC160018)。
文摘With the rapid development of the internet,the dissemination of public opinion in online social networks has become increasingly complex.Existing dissemination models rarely consider group phenomena and the simultaneous spread of competing public opinion information in online social networks.This paper introduces the UHNPR information dissemination model to study the dynamic spread and interaction of positive and negative public opinion information in hypernetworks.To improve the accuracy of modeling of information dissemination,we revise the traditional assumptions of constant propagation and decay rates by redefining these rates based on factors that influence the spread of public opinion information.Subsequently,we validate the effectiveness of the UHNPR model using numerical simulations and analyze the impact of factors such as authority effect,user intimacy,information content and information timeliness on the spread of public opinion,providing corresponding suggestions for public opinion control.Our research results demonstrate that this model outperforms the SIR,SEIR and SEIDR models in describing public opinion propagation in real social networks.Compared with complex networks,information spreads faster and more extensively in hypernetworks.
基金supported in part by the National Natural Science Foundation of China(62033008,62188101,62173343,62073339)the Natural Science Foundation of Shandong Province of China(ZR2024MF072,ZR2022ZD34)the Research Fund for the Taishan Scholar Project of Shandong Province of China.
文摘This article presents an adaptive fault-tolerant tracking control strategy for unknown affine nonlinear systems subject to actuator faults and external disturbances.To address the hyperparameter initialization challenges inherent in conventional neural network training,an improved self-organizing radial basis function neural network(SRBFNN)with an input-dependent variable structure is developed.Furthermore,a novel selforganizing RBFNN-based observer is introduced to estimate system states across all dimensions.Leveraging the reconstructed states,the proposed adaptive controller effectively compensates for all uncertainties,including estimation errors in the observer,ensuring accurate state tracking with reduced control effort.The uniform ultimate boundedness of all closed-loop signals and tracking errors is rigorously established via Lyapunov stability analysis.Finally,simulations on two different nonlinear systems comprehensively validate the effectiveness and superiority of the proposed control approach.
文摘Due to the widespread use of the Internet,customer information is vulnerable to computer systems attack,which brings urgent need for the intrusion detection technology.Recently,network intrusion detection has been one of the most important technologies in network security detection.The accuracy of network intrusion detection has reached higher accuracy so far.However,these methods have very low efficiency in network intrusion detection,even the most popular SOM neural network method.In this paper,an efficient and fast network intrusion detection method was proposed.Firstly,the fundamental of the two different methods are introduced respectively.Then,the selforganizing feature map neural network based on K-means clustering(KSOM)algorithms was presented to improve the efficiency of network intrusion detection.Finally,the NSLKDD is used as network intrusion data set to demonstrate that the KSOM method can significantly reduce the number of clustering iteration than SOM method without substantially affecting the clustering results and the accuracy is much higher than Kmeans method.The Experimental results show that our method can relatively improve the accuracy of network intrusion and significantly reduce the number of clustering iteration.
基金supported by the National Natural Science Foundation of China(Grant Nos.70871082 and 71301104)the Shanghai First-class Academic DisciplineProject,China(Grant No.S1201YLXK)
文摘The principle that ＇the brand effect is attractive＇ underlies the preferential attachment. Here we show that the brand effect is just one dimension of attractiveness. Another dimension is competitiveness. We firstly introduce a general frame- work that allows us to investigate the competitive aspect of real networks, instead of simply preferring popular nodes. Our model accurately describes the evolution of social and technological networks. The phenomenon that more competitive nodes become richer can help us to understand the evolution of many competitive systems in nature and society. In general, the paper provides an explicit analytical expression of degree distributions of the network. In particular, the model yields a nontrivial time evolution of nodes＇ properties and the scale-free behavior with exponents depending on the microscopic parameters characterizing the competition rules. Secondly, through theoretical analyses and numerical simulations, we reveal that our model has not only the universality for the homogeneous weighted network, but also the character for the heterogeneous weighted network. Thirdly, we also develop a model based on the profit-driven mechanism. It can better describe the observed phenomenon in enterprise cooperation networks. We show that the standard preferential attachment, the growing random graph, the initial attractiveness model, the fitness model, and weighted networks can all be seen as degenerate cases of our model.
基金supported by the National Key R&D Program of China (GrantN o.2016YFC0401407)National Natural Science Foundation of China (Grant Nos. 51479003 and 51279006)
文摘Due to rapid urbanization, waterlogging induced by torrential rainfall has become a global concern and a potential risk affecting urban habitant's safety. Widespread waterlogging disasters haveoccurred almost annuallyinthe urban area of Beijing, the capital of China. Based on a selforganizing map(SOM) artificial neural network(ANN), a graded waterlogging risk assessment was conducted on 56 low-lying points in Beijing, China. Social risk factors, such as Gross domestic product(GDP), population density, and traffic congestion, were utilized as input datasets in this study. The results indicate that SOM-ANNis suitable for automatically and quantitatively assessing risks associated with waterlogging. The greatest advantage of SOM-ANN in the assessment of waterlogging risk is that a priori knowledge about classification categories and assessment indicator weights is not needed. As a result, SOM-ANN can effectively overcome interference from subjective factors,producing classification results that are more objective and accurate. In this paper, the risk level of waterlogging in Beijing was divided into five grades. The points that were assigned risk grades of IV or Vwere located mainly in the districts of Chaoyang, Haidian, Xicheng, and Dongcheng.
文摘Fiber reinforced polymers (FRPs), unlike steel, are corrosion-resistant and therefore are of interest;however, their use is hindered because their brittle shear is formulated in most specifications using limited data available at the time. We aimed to predict the shear strength of concrete beams reinforced with FRP bars and without stirrups by compiling a relatively large database of 198 previously published test results (available in appendix). To model shear strength, an artificial neural network was trained by an ensemble of Levenberg-Marquardt and imperialist competitive algorithms. The results suggested superior accuracy of model compared to equations available in specifications and literature.
文摘Internet of things and network densification bring significant challenges to uplink management.Only depending on optimization algorithm enhancements is not enough for uplink transmission.To control intercell interference,Fractional Uplink Power Control(FUPC)should be optimized from network-wide perspective,which has to find a better traffic distribution model.Conventionally,traffic distribution is geographic-based,and ineffective due to tricky locating efforts.This paper proposes a novel uplink power management framework for Self-Organizing Networks(SON),which firstly builds up pathloss-based traffic distribution model and then makes the decision of FUPC based on the model.PathLoss-based Traffic Distribution(PLTD)aggregates traffic based on the propagation condition of traffic that is defined as the pathloss between the position generating the traffic and surrounding cells.Simulations show that the improvement in optimization efficiency of FUPC with PLTD can be up to 40%compared to conventional GeoGraphic-based Traffic Distribution(GGTD).
基金supported by National Natural Science Foundation of China (Grant No 60674026)the Jiangsu Provincial Natural Science Foundation of China (Grant No BK2007016)Program for Innovative Research Team of Jiangnan University of China
文摘This paper studies the global exponential stability of competitive neural networks with different time scales and time-varying delays. By using the method of the proper Lyapunov functions and inequality technique, some sufficient conditions are presented for global exponential stability of delay competitive neural networks with different time scales. These conditions obtained have important leading significance in the designs and applications of global exponential stability for competitive neural networks. Finally, an example with its simulation is provided to demonstrate the usefulness of the proposed criteria.
基金supported by the National Social Science Fund of China (Grant No.23BGL270)。
文摘The virtuality and openness of online social platforms make networks a hotbed for the rapid propagation of various rumors.In order to block the outbreak of rumor,one of the most effective containment measures is spreading positive information to counterbalance the diffusion of rumor.The spreading mechanism of rumors and effective suppression strategies are significant and challenging research issues.Firstly,in order to simulate the dissemination of multiple types of information,we propose a competitive linear threshold model with state transition(CLTST)to describe the spreading process of rumor and anti-rumor in the same network.Subsequently,we put forward a community-based rumor blocking(CRB)algorithm based on influence maximization theory in social networks.Its crucial step is to identify a set of influential seeds that propagate anti-rumor information to other nodes,which includes community detection,selection of candidate anti-rumor seeds and generation of anti-rumor seed set.Under the CLTST model,the CRB algorithm has been compared with six state-of-the-art algorithms on nine online social networks to verify the performance.Experimental results show that the proposed model can better reflect the process of rumor propagation,and review the propagation mechanism of rumor and anti-rumor in online social networks.Moreover,the proposed CRB algorithm has better performance in weakening the rumor dissemination ability,which can select anti-rumor seeds in networks more accurately and achieve better performance in influence spread,sensitivity analysis,seeds distribution and running time.
基金the National Natural Science Foundation of China (No.61627810)the National Science and Technology Major Program of China (No.2018YFB1305003)the National Defense Science and Technology Outstanding Youth Science Foundation (No.2017-JCJQ-ZQ-031)。
文摘Traveling salesman problem(TSP)is a classic non-deterministic polynomial-hard optimization prob-lem.Based on the characteristics of self-organizing mapping(SOM)network,this paper proposes an improved SOM network from the perspectives of network update strategy,initialization method,and parameter selection.This paper compares the performance of the proposed algorithms with the performance of existing SOM network algorithms on the TSP and compares them with several heuristic algorithms.Simulations show that compared with existing SOM networks,the improved SOM network proposed in this paper improves the convergence rate and algorithm accuracy.Compared with iterated local search and heuristic algorithms,the improved SOM net-work algorithms proposed in this paper have the advantage of fast calculation speed on medium-scale TSP.
基金National Natural Science Foundation ofChina!( No.69672 0 0 7)
文摘On the basis of asymptotic theory of Gersho, the isodistortion principle of vector clustering was discussed and a kind of competitive and selective learning method (CSL) which may avoid local optimization and have excellent result in application to clusters of HMM model was also proposed. In combining the parallel, self organizational hierarchical neural networks (PSHNN) to reclassify the scores of every form output by HMM, the CSL speech recognition rate is obviously elevated.
基金Supported by the National"Fifteenth Year Plan"Key Project(2001BA307B01 02 01)
文摘Introduce a method of generation of new units within a cluster and aalgorithm of generating new clusters. The model automatically builds up its dynamically growinginternal representation structure during the learning process. Comparing model with other typicalclassification algorithm such as the Kohonen's self-organizing map, the model realizes a multilevelclassification of the input pattern with an optional accuracy and gives a strong support possibilityfor the parallel computational main processor. The idea is suitable for the high-level storage ofcomplex datas structures for object recognition.
基金supported by the National Natural Science Foundation of China (Grant No 60672095)the National High-Tech Research and Development Program of China (Grant No 2007AA11Z210)+3 种基金the Doctoral Fund of Ministry of Education of China (Grant No 20070286004)the Natural Science Foundation of Jiangsu Province,China (Grant No BK2008281)the Science and Technology Program of Southeast University,China (Grant No KJ2009351)the Excellent Young Teachers Program of Southeast University,China (Grant No BG2007428)
文摘The shortcomings of traditional methods to find the shortest path are revealed, and a strategy of finding the self- organizing shortest path based on thermal flux diffusion on complex networks is presented. In our method, the shortest paths between the source node and the other nodes are found to be self-organized by comparing node temperatures. The computation complexity of the method scales linearly with the number of edges on underlying networks. The effects of the method on several networks, including a regular network proposed by Ravasz and Barabasi which is called the RB network, a real network, a random network proposed by Ravasz and Barabasi which is called the ER network and a scale-free network, are also demonstrated. Analytic and simulation results show that the method has a higher accuracy and lower computational complexity than the conventional methods.
文摘The typical characteristic of the topology of Bayesian networks (BNs) is the interdependence among different nodes (variables), which makes it impossible to optimize one variable independently of others, and the learning of BNs structures by general genetic algorithms is liable to converge to local extremum. To resolve efficiently this problem, a self-organizing genetic algorithm (SGA) based method for constructing BNs from databases is presented. This method makes use of a self-organizing mechanism to develop a genetic algorithm that extended the crossover operator from one to two, providing mutual competition between them, even adjusting the numbers of parents in recombination (crossover/recomposition) schemes. With the K2 algorithm, this method also optimizes the genetic operators, and utilizes adequately the domain knowledge. As a result, with this method it is able to find a global optimum of the topology of BNs, avoiding premature convergence to local extremum. The experimental results proved to be and the convergence of the SGA was discussed.
文摘Haze-fog,which is an atmospheric aerosol caused by natural or man-made factors,seriously affects the physical and mental health of human beings.PM2.5(a particulate matter whose diameter is smaller than or equal to 2.5 microns)is the chief culprit causing aerosol.To forecast the condition of PM2.5,this paper adopts the related the meteorological data and air pollutes data to predict the concentration of PM2.5.Since the meteorological data and air pollutes data are typical time series data,it is reasonable to adopt a machine learning method called Single Hidden-Layer Long Short-Term Memory Neural Network(SSHL-LSTMNN)containing memory capability to implement the prediction.However,the number of neurons in the hidden layer is difficult to decide unless manual testing is operated.In order to decide the best structure of the neural network and improve the accuracy of prediction,this paper employs a self-organizing algorithm,which uses Information Processing Capability(IPC)to adjust the number of the hidden neurons automatically during a learning phase.In a word,to predict PM2.5 concentration accurately,this paper proposes the SSHL-LSTMNN to predict PM2.5 concentration.In the experiment,not only the hourly precise prediction but also the daily longer-term prediction is taken into account.At last,the experimental results reflect that SSHL-LSTMNN performs the best.
基金Supported by National Nature Science Foundation of China (No.60573072)
文摘Because interval value is quite natural in clustering, an interval-valued fuzzy competitive neural network is proposed. Firstly, this paper proposes several definitions of distance relating to interval number. And then, it indicates the method of preprocessing input data, the structure of the network and the learning algorithm of the interval-valued fuzzy competitive neural network. This paper also analyses the principle of the learning algorithm. At last, an experiment is used to test the validity of the network.
文摘The territorial consideration is enclosed in the development of network economy and e-Business, and there is no departure of the cultivation of e-Business from the establishment of territorial competitiveness. The traditional theory of the territorial competitiveness has come across a serious impact from the Internet. So the development of Intemet infrastructures and institutional environment would be something new to the theory of territorial competitiveness in the Internet era.
文摘Combining with the characters of the practicing qualification personnel in construction market,evaluation method based on the self-organizing neural network is brought out to analyze the credit classification of the practicing qualification personnel. And the impact factors on the credit classification of the practicing qualification personnel,such as the number of neurons,the training steps,the dimension of neurons and the field of winning neurons are studied. Then a self-organizing competitive neural network is built. At last,a case study is conducted by taking practicing qualification personnel as an example. The research result reveals that the method can efficiently evaluate the credit of the practicing qualification personnel;thus,it could provide scientific advice to the construction enterprise to prevent relevant discreditable behaviors of some practicing qualification personnel.
文摘The artificial neural networks (ANNs), among different soft computing methodologies are widely used to meet the challenges thrown by the main objectives of data mining classification techniques, due to their robust, powerful, distributed, fault tolerant computing and capability to learn in a data-rich environment. ANNs has been used in several fields, showing high performance as classifiers. The problem of dealing with non numerical data is one major obstacle prevents using them with various data sets and several domains. Another problem is their complex structure and how hands to interprets. Self-Organizing Map (SOM) is type of neural systems that can be easily interpreted, but still can’t be used with non numerical data directly. This paper presents an enhanced SOM structure to cope with non numerical data. It used DNA sequences as the training dataset. Results show very good performance compared to other classifiers. For better evaluation both micro-array structure and their sequential representation as proteins were targeted as dataset accuracy is measured accordingly.
