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This paper explores the law of emotion transmission on the social Internet. By applying higher-order network theory, ER random networks are reconstructed and higher-order network structures with multiple interaction points are generated. The I-state of the SI model is extended to set up the propagation rules of emotions based on higher-order networks. Concepts such as sentiment recession rate are introduced to make sentiment propagate through different simplexes, so as to obtain the propagation rules of higher-order networks. Qualitative analysis of emotional changes during the propagation process verifies the phase transition phenomenon of emotions in the network. By comparing simulated results with actual data, we demonstrate the accuracy of our higher-order network-based emotion propagation model in reflecting real-world trends and outcomes. The study also explores the effects of factors such as the initial proportion of extreme emotions on emotion propagation. This study provides valuable insights into understanding the operation of complex systems and offers important implications for government predictions of opinion development. [ABSTRACT FROM AUTHOR]

The complexity of systems stems from the richness of the group interactions among their units. Classical networks exhibit identified limits in the study of complex systems, where links connect pairs of nodes, inability to comprehensively describe higher-order interactions in networks. Higher-order networks can enhance modeling capacities of group interaction networks and help understand and predict network dynamical behavior. This paper constructs a social hypernetwork with a group structure by analyzing a community overlapping structure and a network iterative relationship, and the overlapping relationship between communities is logically separated. Considering the different group behavior pattern and attention focus, we defined the group cognitive disparity, group credibility, group cohesion index, hyperedge strength to study the relationship between information dissemination and network evolution. This study shows that groups can alter the connected network through information propagation, and users in social networks tend to form highly connected groups or communities in information dissemination. Propagation networks with high clustering coefficients promote the fractal information dissemination, which in itself drives the fractal evolution of groups within the network. This study emphasizes the significant role of "key groups" with overlapping structures among communities in group network propagation. Real cases provide evidence for the clustering phenomenon and fractal evolution of networks. [ABSTRACT FROM AUTHOR]

This paper focuses on the internal law of online public opinion spreading under the conditions of different types of spreaders by constructing the model of public opinion spreading by subtypes of spreaders. The new S I S I O I N model is constructed by abstracting factors such as netizens' interests, emotions and views on hot events in the Internet into propensity indices. The simulation results show that the spread of public opinion after categorizing netizens is more in line with the spread of public opinion in reality. Moreover, netizens tend to show different spreading behaviors in different public opinion cases, and the degree of change in netizens' propensity varies greatly. All the data used in the simulation experiments are real data and analyzed with real events. The research results are helpful for what kind of control strategies are adopted after the occurrence of hotspot events. [ABSTRACT FROM AUTHOR]

In real network systems, there are numerous information propagation phenomena. However, the impact of homogeneity on information propagation is still not well understood. In this paper, we introduce a dynamic information propagation model named H-SIR. We also incorporate the propagation mechanism of content homogeneity and define the dynamic propagation rate based on users' interest locking and fatigue level. Through simulation experiments, we demonstrate how homogeneous information spreads and evolves on the network. Furthermore, we compare the model with real data of COVID-19 information on the Sina-Weibo social platform and the traditional SIR model to jointly validate our model. Our results show that in social networks, if the themes of homogeneous information are diverse, even with an increasing amount of homogeneous information, it will hardly affect the propagation of this type of information. However, if users are recommended excessive homogeneous information, they will not focus entirely but instead display selective contact behavior. In the case of information fatigue, a type of information with a low degree of homogeneity will survive longer than a type of information with a high degree of homogeneity. Finally, we propose corresponding strategies to alleviate the phenomenon of information homogenization in social networks. [ABSTRACT FROM AUTHOR]

The industry is the key to economic development. This paper proposed a fractal evolutionary dynamic model of industry based on the propagation dynamics theory from the perspective of fractal theory. The model quantitatively portrays the evolutionary trend of industries and provides reasonable solutions to the problems faced by industrial structure optimization. First, the fractal characteristics of the industrial system are analyzed, and the proposed model is used to study the evolution of industry and enterprise impact values at different granularities. Subsequently, the impact of micro firms on the macro industrial development was further investigated. Finally, the correctness of the model simulation results was verified by real data. The results showed that the evolution of industries under different granularities shows self-similarity and that micro industries with small and medium-sized enterprises (SMEs) were more influential in the development of industries. Finally, the proposed fractal evolutionary dynamics model of the industry has proven to be reasonable and effective through empirical comparison. This study provided new insights for solving industrial development problems with high theoreticality and reliability. [ABSTRACT FROM AUTHOR]

In the research of network structure, long ties are considered to be a hidden but valuable interaction. In this paper, we innovatively interpret the long ties structure in traditional research as a higher-order information spreading path. It effectively avoids the homogenization of information level and redundancy of network structure. The spreading bridge model based on high-order long ties structure is established accordingly. Modern online social behavior is modeled by establishing spreading expansion rate and spreading diffusion rate to improve model adaptability. The spread dynamics model based on multivariate interactions is developed by combining direct-linked spread as a binary interaction relationship in traditional research with bridge-linked spread. The validity of the model is verified by comparing the model simulation results with real cases. [ABSTRACT FROM AUTHOR]

The interaction of information and the evolution of network structure are inseparable. In order to construct social network evolution and information propagation models that better fit real-world scenarios, this paper proposes a social network structure evolution model driven by changes in the strength of relationships between individuals through their information interactions with each other. During the evolution process of the network, information interaction between individuals is also influenced by the network structure. Therefore, we improve traditional propagation models and construct an information propagation model with dynamic propagation rates. The proposed model is used to simulate both the spread of information and the evolution of network structures in real social networks. Finally, simulation results are compared to real-world data, demonstrating the effectiveness and rationality of the proposed model. [ABSTRACT FROM AUTHOR]

This paper investigates the factors influencing the spread of popular topic competition from the perspective of propagation dynamics. The popularity of a topic is directly influenced by the level of people's interest in it. The rarity, deviation and type can influence the level of interest in such topics. In addition, the topic publisher also has an impact on the competitive dissemination of the topic. The higher the rank of the original publisher is, the easier it spreads. Compared with small bloggers without fans, it is easier to attract people's attention, and the heat of the topic will rise faster. The results show that the rarer the topic, the greater the deviation from reality, and the more followers the originator has, the more competitive it is. [ABSTRACT FROM AUTHOR]

In this paper, first, the structure of social networks was investigated from fractal perspective and the fractal network was built from online social networks. Then inspired by the flock effect, the characteristics of "separation", "cohesion" and "alignment" in information propagation were studied from the inherent granularity structure of fractal communities. Accordingly, the agreement degree and controversy degree between community members at different granularities were defined. And the "Seeking Common Ground While Shelving Differences" propagation rule in fractal network was presented. On this basis, a new S2I propagation model was proposed. From this model, communicators were divided into two states, I and OI. People in status I have a tendency to express support for a message. While those in state OI have a tendency to oppose it. The information propagation process and characteristics were analyzed further by using S2I model. Finally, the effectiveness of S2I model was verified by comparative experiments of simulation and real data analysis. [ABSTRACT FROM AUTHOR]

In this paper, a two-layer network on various immunization strategies in the post-epidemic era is constructed and an improved symbiotic evolutionary model of COVID-19 and information collaboration is proposed. The dynamic transformation probability is introduced to influence the virus information transmission coevolutionary process. The dynamic transformation probability is influenced by the immunization strategies and vertex characteristics. We quantify the effects of immunization strategy, node properties, global temperature, and collaborative information dissemination on new crown outbreaks. We simulated our model in a scale-free network to analyze the propagation. The evolutionary phenomenon of the network during propagation was investigated. We simulated the proven epidemic information coevolutionary model in a two-layer network, validated it with real data comparisons by proving that our proposed model fits the real situation. [ABSTRACT FROM AUTHOR]

This paper takes COVID-19-related online rumors as the research object, and explores the law of spreading public opinion in social networks. The paper also conducts empirical research on the relationship between rumor spreading, user characteristics and subject interest differences, and analyzes the common influence of individual factors and social environment. In the process of public opinion dissemination, measures that can effectively regulate the dissemination of public opinion are proposed. Based on the susceptible-exposed-infected-recovered (SEIR) model, this paper analyzes the influence of individual differentiation characteristics, friend factors, and time-dependent decline on user status changes. The study found that the user's environment can affect the spread and popularity of public opinion information, and prolong the survival time of public Controlling the propagation threshold and exit threshold of the platform helps to control the large-scale dissemination of online public opinion. The extinction of public opinion is affected by the decline of time and heat rather than certain probability. [ABSTRACT FROM AUTHOR]

This paper takes COVID-19-related online rumors as the research object, and explores the law of spreading public opinion in social networks. The paper also conducts empirical research on the relationship between rumor spreading, user characteristics and subject interest differences, and analyzes the common influence of individual factors and social environment. In the process of public opinion dissemination, measures that can effectively regulate the dissemination of public opinion are proposed. Based on the susceptible-exposed-infected-recovered (SEIR) model, this paper analyzes the influence of individual differentiation characteristics, friend factors, and time-dependent decline on user status changes. The study found that the user's environment can affect the spread and popularity of public opinion information, and prolong the survival time of public Controlling the propagation threshold and exit threshold of the platform helps to control the large-scale dissemination of online public opinion. The extinction of public opinion is affected by the decline of time and heat rather than certain probability. [ABSTRACT FROM AUTHOR]

In view of the fractal evolution phenomenon in Internet information dissemination, this paper constructs a generalized fractal network by analyzing the characteristics of user dissemination behavior and according to the dissemination trend of actual information. On this basis, it reveals the fractal evolution mechanism in Internet information dissemination and its dissemination dynamic characteristics and rules. First, based on node similarity, a fractal propagation model of Internet information is constructed. Second, based on the scale-free network and according to the user characteristics, the information dissemination process is simulated and a generalized fractal network is evolved. Then, the key factors influencing the formation of fractal networks, the characteristics, and the rules of propagation dynamics are analyzed. Finally, the rationality and validity of the model are verified by crawling the real user forwarded data in the micro-blog. [ABSTRACT FROM AUTHOR]

A new model of human flesh search (HFS) was established based on the love-triangle model (LTM) and susceptible-infected-recovered (SIR) model. The competitive propagation process between rumors and true messages in HFS was investigated. According to the proposed model, the equation was derived to describe the propagation of rumors and true messages in HFS. Coupling effect of rumors and true messages and the node credibility were considered; the simulations were implemented on small world network. The results show that the competition probability initial value of rumors is higher than that of true messages in the process of HFS. The coupling effect indicates that the spreading of one information fragment would be influenced by others. Furthermore, the inhibition of credibility on rumor spreading is found. Therefore, the means of suppressing rumors is proposed furtherly. Finally, the validity of the new model was verified through the comparative experiment between the simulation of the proposed model and real case. [ABSTRACT FROM AUTHOR]

Inspired by infectious disease dynamics and modern psychology, this paper aims at constructing a multi-dimensional function to get the model of information dissemination on social networks under epidemic-related panic base on the characteristics of individual differences and global characteristics, like emotional cumulative effect, herd effect, time-sensitive decline effect, cognitive level, intimacy, personal influence, etc. The results show that the psychological effect has a significant effect on the increase of the spread of panic news; When netizens are in an emotional atmosphere, their emotional self-regulation ability is limited; when the infection rate is relatively low, the characteristics of individual differences play a leading role in affecting the spreading process. When the infection rate is high enough, the herd effect and emotional cumulative effect play a major role in promoting information dissemination; In a society with a higher rate of emotional contact, it is easier to form a kind of collective wisdom, which can help the collective quickly identify rumors. Moreover, in this kind of society, the role of opinion leaders is limited, and timely refutation of rumors can significantly reduce the spread of panic news. [ABSTRACT FROM AUTHOR]

The evolution analysis of community structure of social network will help us understand the composition of social organizations and the evolution of society better. In order to discover the community structure and the regularity of community evolution in large-scale social networks, this paper analyzes the formation process and influencing factors of communities, and proposes a community evolution analysis method of crowd attraction driven. This method uses the traditional community division method to divide the basic community, and introduces the theory of information propagation into complex network to simulate the information propagation of dynamic social networks. Then defines seed node, the activity of basic community and crowd attraction to research the influence of groups on individuals in social networks. Finally, making basic communities as fixed groups in the network and proposing community detection algorithm based on crowd attraction. Experimental results show that the scheme can effectively detect and identify the community structure in large-scale social networks. [ABSTRACT FROM AUTHOR]

In this paper, the epidemic spreading was investigated from the point of view of fractal. Firstly, the real network was abstracted as a fully connected fractal network. Based on the fractal network, the fractal spreading process was studied. The fractal spreading process was simulated to analyze the change of infection density during transmission. The results showed that the infection density presented an upward trend of the ladder-shaped, and a jumping change in infection density occurred during a certain time. As an illustration, the pandemic of COVID-19 was analyzed, the results indicated that the proposed method was valid. Experiments and analyses have shown that intervention at key jump points in virus propagation can effectively control the spread of the virus. [ABSTRACT FROM AUTHOR]

The influence of node behavior by the relevant group behavior in complex networks is a topic of recent interest. In order to measure the direct and indirect influence of the neighborhoods, the behavioral propagation and competition model was established based on the pressure. The pressure is described by the impact of group behavior on nodes, which is related to the length and number of reachable paths between two nodes for measuring the nodal behavioral influence. In addition, the pressure range has an effect on the pressure. By modeling and analyzing the change of nodes motivation and the rules of behavioral propagation, and numerical simulations are performed on the small-world networks and the scale-free networks. The results show that pressure is the major factor in the node behavioral motivation, where the pressure generated from behavior in related group network is dependent on the relative location and number of participators. At the same time, network structure also plays an important role at behavior propagation process. Further, competition arises when multiple behaviors are spread among people, while winning behaviors are widely spread among people. [ABSTRACT FROM AUTHOR]

This paper presents a new SIRI propagation model based on SIR model. Behavioral motivation value and neighbor infection number influence value were added to the model to study the effects of these two quantities on behavior transmission. In order to make the model closer to the real situation, self-motivation value and neighbor influence value are added to the behavior motivation value. Compared with previous studies, our proposed experimental model is closer to the real world, improves the possibility of behavior prediction, and is validated by simulation. Finally, this paper takes Lanzhou Polytechnic University campus cartoon consumption data as an example to test and validate the model. [ABSTRACT FROM AUTHOR]

The iterative propagation of information between nodes will strengthen the connection strength between nodes, and the network can evolve into different groups according to difference edge strength. Based on this observation, we present the user engagement to quantify the influences of users different propagation modes to network propagation, and construct weight network to simulate real social network, and proposed the community detection method in social networks based on information propagation and user engagement. Our method can produce different scale communities and overlapping community. We also applied our method to real-world social networks. The experiment proved that the network spread and the community division interact with each other. The community structure is significantly different in the network propagation of different scales. [ABSTRACT FROM AUTHOR]

Recently, the study about the disease transmission has received widespread attention. In the dynamics process of infectious disease, individual's cognition about disease-related knowledge is an important factor that controls disease transmission. The disease-related information includes the cause, symptoms, transmission route and so on. Disease-related knowledge would influence the individual's attitude toward disease, and influence the transmission rate and scale of the infectious disease. In order to study the impact of individual cognition on the transmission of disease, the disease transmission model based on individual cognition is proposed in this paper. Based on this model, we numerically simulate the transmission of disease in the small-world network and the BA scale-free network, respectively, and analyze the transmission dynamics behavior of the infectious disease. The simulation experiment verifies the validity of the theoretical result, which shows that this model is closer to the reality than traditional models. [ABSTRACT FROM AUTHOR]

In this study, the incentive is defined based on feedback mechanism promoting propagation, and a novel model of behavior propagation is proposed based on feedback mechanism to investigate behavior propagation. In this model, the incentive includes the distance among individuals and the set of state and the influence of node is decided by the incentive and node degree, which explore the propagation effect under different network. The experimental results show the incentive and the propagation have positive correlation, and the propagating effect is determined by the network attribute. At the same time, the greater the degree of the node, the more obvious the incentive effect. Incentive results of nodes will continuously decline until they suffer second incentive. [ABSTRACT FROM AUTHOR]

The phase transition in information spreading was investigated. Two phase transitions were studied, respectively, from the point of view of network information and the node information, and the corresponding simulations were implemented on high-order networks. In order to explore the phase transition in information dissemination, this paper analyzes the characteristics of social networks and constructs a high-order network model with explicit, subexplicit, and implicit relationships. The factors affecting interpersonal communication were analyzed, the social cumulative effect was introduced, and two transmission models with characteristics of permanent forgetting and temporary forgetting were proposed. The comparison between simulated experiment and real case shows that the network mode accord with the characteristics of the actual network, and the two models of phase transitions are closer to the actual. [ABSTRACT FROM AUTHOR]

Combined with RBF neural network and sliding mode control, the synchronization between drive system and response system was achieved in module space and phase space, respectively (module-phase synchronization). The RBF neural network is used to estimate the unknown nonlinear function in the system. The module-phase synchronization of two fractional-order complex chaotic systems is implemented by the Lyapunov stability theory of fractional-order systems. Numerical simulations are provided to show the effectiveness of the analytical results. [ABSTRACT FROM AUTHOR]

In real life, the propagation ability of the information disseminator is one of the important factors which is determined to propagate information. The influence of the node, which is altered with time, is proposed to reflect the propagation ability of the information disseminator for the significance of the information propagation in the actual situation in this paper. Therefore, the influence of the node is divided into the high-impact node and the low-impact node. Furthermore, the SSIR information propagation model is proposed and the dynamic BA scale-free network is constructed to carry out evolution of node impact based on secondary propagation experiments. The experiment results indicate three stages, including the initial stage, the rapidly rising stage and the stable stage. The propagation details of the different messages are distinct. However, the trend of propagation is similar. [ABSTRACT FROM AUTHOR]

This paper aims at the phenomenon of information selective propagation based on historical memory. A network model with memory strength and edge strength is established. The information propagation model with memory-clustering ability is designed with SIR model. And unsupervised learning is introduced to modify the performance. Based on the new network model, the core network and critical path that play a key role in the information propagation are found through the K-shell decomposition method. The research shows that the memory network contains an inertial channel for information propagation, it makes information propagation smooth. And information is selectively propagated in the new network, information is more inclined to propagate between nodes with powerful memory strength and close connections, in other words, people are more willing to propagate information to old friends who have been in contact for a long time instead of new friends. [ABSTRACT FROM AUTHOR]

Abstract Different from traditional multi-community, the paper proposes a new multi-relationship network which is constructed to assess the effect of interlaced complex interpersonal relationships on the epidemic spreading process. Besides, an innovative MR-SIS epidemic model is proposed and analyzed in this paper. Empirically, we simulate MR-SIS spreading process on a multi-relationship network. The experiment results show that immunizing the nodes in overlapping sub-networks inhibits the epidemic spreading. Moreover, when considering the multi-relationship network structure and the above conclusion, this paper proposes a novel immunization to overcome the limitations of traditional immunization strategy, which has been proven a satisfying desirable performance on controlling disease transmission. [ABSTRACT FROM AUTHOR]

In this paper, a new spreading network was constructed and the corresponding immunizations were proposed. The social ability of individuals in the real human social networks was reflected by the node strength. The negativity and positivity degrees were also introduced. And the edge weights were calculated by the negativity and positivity degrees, respectively. Based on these concepts, a new asymmetric edge weights scale-free network which was more close to the real world was established. The comparing experiments indicate that the proposed immunization is priority to the acquaintance immunization, and close to the target immunization. [ABSTRACT FROM AUTHOR]

In the propagation efficiency point of view, the node credibility is introduced in this paper. For the message receiver, the node would partially believe the message according to the credibility of the propagator. For a node, the credibility is variable. The more the true message spread, the higher the credibility, and vice versa, the credibility becomes smaller. Based on the idea, a new network was established with the node credibility. Finally, a comparing experiment between the fully trusted network and the network with the node credibility was implemented. The results indicate that the spread effect of messages is better in the network with the node credibility. [ABSTRACT FROM AUTHOR]

Based on the stress responses of individuals, the susceptible-infected-susceptible epidemic model was improved on the small-world networks and BA scale-free networks and the simulations were implemented and analyzed. Results indicate that the behaviors of individual's stress responses could induce the epidemic spreading resistance and adaptation at the network level. This phenomenon showed that networks were learning how to adapt to the disease and the evolution process could improve their immunization to future infectious diseases and would effectively prevent the spreading of infectious diseases. [ABSTRACT FROM AUTHOR]

Investor behavior has a pivotal role in price dynamics. The aim of this study was to explore the characteristics and patterns of the investor behavior and network structure behind. The investor behavior propagation model was proposed, which described the intrinsic dynamic mechanism of stock market and had a good fit to the real data. It was found that the Nearest-neighbor coupled network and the disassortative network can depict the investing behavior network precisely. Combining these two aspects, the Nearest-neighbor coupled network with hub nodes was constructed. Performance was superior to other network structures and suggests that the Nearest-neighbor coupled network with hub nodes is more consistent with the real investor networks. • An investor behavior propagation model is constructed from price dynamics and epidemic model. • Some characteristics of propagation behavior in the investor network are discovered. • The propagation dynamic mechanism of stock market is revealed. • Nearest-neighbor coupled network with hub nodes is more consistent with the real investor network. [ABSTRACT FROM AUTHOR]

The real network nodes are always interfered by other messages. So, how to realize the hybrid synchronization of two independent chaotic systems based on the complex network is very important. To solve this problem, two other problems should be considered. One is how the same network node of the complex network was affected by different information sources. Another is how to achieve hybrid synchronization on the network. In this paper, the theoretical analysis and numerical simulation on various complex networks are implemented. The results indicate that the hybrid synchronization of two independent chaotic systems is feasible. [ABSTRACT FROM AUTHOR]

In this paper, a new immunization strategy was established to prevent the epidemic spreading based on the principle of "Multi-granularity" and "Pre-warning Mechanism", which send different pre-warning signal with the risk rank of the susceptible node to be infected. The pre-warning means there is a higher risk that the susceptible node is more likely to be infected. The multi-granularity means the susceptible node is linked with multi-infected nodes. In our model, the effect of the different situation of the multi-granularity immunizations is compared and different spreading rates are adopted to describe the epidemic behavior of nodes. In addition the threshold value of epidemic outbreak is investigated, which makes the result more convincing. The theoretical analysis and the simulations indicate that the proposed immunization strategy is effective and it is also economic and feasible. [ABSTRACT FROM AUTHOR]

A high precision fast projective synchronization method for chaotic systems with unknown parameters was proposed by introducing optimal matrix. Numerical simulations indicate that the precision be improved about three orders compared with other common methods under the same condition of software and hardware. Moreover, when average error is less than 10-3, the synchronization speed is 6500 times than common methods, the iteration needs only 4 times. The unknown parameters also were identified rapidly. The theoretical analysis and proof also were given. [ABSTRACT FROM AUTHOR]

A projective synchronization of chaotic complex system was investigated. First, the cross projective synchronization (the real part and the imaginary part synchronize up to different scaling factors, respectively) in coupled partially linear complex nonlinear dynamic system was realized without adding any control term. Then, we investigated the substantial conditions of projective synchronization of chaotic complex systems on whole state variable with time delay. The adaptive controllers were designed. Moreover, numerical simulations were given to confirm the analytical results. [ABSTRACT FROM AUTHOR]

This paper investigates the generalized projective synchronization in general autonomous chaotic system. A universal controller is designed and the effectiveness is verified via theoretical analysis and numerical simulations. The controller design is irrelevant to concrete system structure and initial values. It has strong robustness and broad application perspective. [ABSTRACT FROM AUTHOR]

In this paper, pinning synchronization on directed network was considered. By analyzing, some general synchronization criteria on directed network were established. And then, we verified it on directed globally coupled network, directed scale-free network, and directed small-world network, respectively. The pinning nodes were selected, respectively, according to order of in-degrees and out-degrees. Through comparing and analyzing simulations, the optimal pinning scheme was found, and a practical principle was induced finally. [ABSTRACT FROM AUTHOR]

Abstract: The concept of module-phase synchronization was proposed. The chaos synchronization between drive system and response system was achieved in module space and phase space respectively (module-phase synchronization). Different from the evolutions in real space, there is no pseudorandom behavior in phase space when module-phase synchronization achieve. All the phases of complex state variables switched between two fixed values which are determined by initial values of drive system. And the modules varied within a bounded field. The theoretical analysis and the simulations were also given. [Copyright &y& Elsevier]

Abstract: In this paper, we proposed an efficient immunization—“high-risk immunization”. The standard SIRS model was modified, respectively, on WS small-world network and BA scale-free network. Based on our new SIRS model, the density of infected individuals was analyzed from a theoretical point of view, and computer simulation was implemented on different networks. The results indicate that the high-risk immunization is effective, and it is economic and feasible in practice. [Copyright &y& Elsevier]

Abstract: A high precision fast projective synchronization method was proposed through introducing an impact factor. The numerical simulations indicate that the synchronization precision was increased three orders of magnitude higher than available methods in literature, under the same conditions of numerical method and hardware. The synchronization can achieved after first iteration. The synchronization speed has been increased by 13 484 times and 23 000 times when and , respectively, compared to general methods. [Copyright &y& Elsevier]

Abstract: A simple and systematic approach is developed for modeling and neural adaptive backstepping control of an uncertain chaotic system, using only input–output data obtained from the underlying dynamical systems. Gaussian fuzzy membership functions are used in conjunction with the least-squares principle for modeling and control. Based on the dynamic fuzzy neural network (DFNN) modeling, an adaptive backstepping controller is devised, which works through structure and parameter-learning phases for adaptation. The DFNN implements Takagi–Sugeno–Kang fuzzy systems based on extended radial basis function (RBF) neural networks. The design procedure is illustrated by using the multiscroll chaotic attractors as an example, on which simulation results demonstrate the effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]