2,312 publications from this institution
The basic objective of system modeling is to establish an input-output representative mapping that can satisfactorily describe the system behaviors, by using the available input-output data based upon physical or empirical knowledge about the structure of the unknown system.
In recent years secret permutations have been widely used for protecting dierent types of multimedia data, including speech files, digital images and videos. Based on a general model of permutation-only multimedia ciphers, this paper performs a quantitative cryptanalysis on the performance of these kind of ciphers against plaintext attacks. When the plaintext is of size M ◊N and with L dierent levels of values, the following quantitative cryptanalytic findings have been concluded under the assumption of a uniform distribution of each element in the plaintext: 1) all permutation-only multimedia ciphers are practically insecure against known/chosen-plaintext attacks in the sense that only O(log L (MN)) known/chosen plaintexts are sucient to recover not less than (in an average sense) half elements of the plaintext; 2) the computational complexity of the known/chosen-plaintext attack is only O(n·(MN) 2 ), where n is the number of known/chosen plaintexts used. When the plaintext has a non-uniform distribution, the number of required plaintexts and the computational complexity is also discussed. Experiments are given
This letter studies the pinning controllability of asymmetrical weighted scale-free networks, in which every individual node is a chaotic Lorenz system. The selective pinning scheme, which gives the control priority to those nodes with largest numbers of connections, is adopted. Study shows that a dynamical network of this type can be controlled via selective pinning only when the proportion p of the controlled nodes exceeds a critical value pc; and the network pinning controllability is enhanced as p increases for p>pc. This study also reveals the effects of the weights and the topologies of this type of networks on their pinning controllability, showing that pinning controllability of a network can be improved with an asymmetrical weight distribution, where the couplings from older nodes to younger ones are dominant, with a larger average connectivity or with a more heterogeneous connection distribution.
It was reported in the literature that some nonlinear wave equations have the so-called loop- and inverted-loop-soliton solutions, as well as the so-called loop-periodic solutions. Are these true mathematical solutions or just numerical artifacts? To answer the question, this article investigates all traveling wave solutions in the parameter space for three typical nonlinear wave equations from a theoretical viewpoint of dynamical systems. Dynamical analysis shows that all these loop- and inverted-loop-solutions are merely visual illusion of numerical artifacts. To reveal the nature of such special phenomena, this article also offers the mathematical parametric representations of these traveling wave solutions precisely in analytic forms.
We investigate the flocking problem of multiple nonlinear dynamical mobile agents with a virtual leader in a dynamic proximity network. We assume that only a fraction of agents in the network are informed and propose a connectivity-preserving flocking algorithm. Under the assumption that the initial network is connected, we introduce local adaptation strategies for both the weights on the velocity navigational feedback and the coupling strengths that enable all agents to track the virtual leader, without requiring the knowledge of the agent dynamics. The resulting flocking algorithm works even for the case where only one agent is informed.
This paper investigates the problem of network-based leader-following consensus of nonlinear multi-agent systems via distributed impulsive control. First, by taking network-induced delays into account, a nonlinear system with delayed impulses is formulated. Then, a general consensus criterion is derived and several special cases of network-induced delays and network topologies are discussed. Moreover, sufficient conditions on the design of the sampling period, pinned nodes and the coupling strength are provided. The effects of the coupling strength and pinning strategy are further explored for multi-agent systems with an undirected communication graph. Finally, two examples are given to verify the theoretical results.
This paper investigates the competitive relationship and rivalry of industrial markets, using Chinese household electrical appliance firms as a platform for the study. The common complex network models belong to one-level networks in layered classification, while this paper formulates and evaluates a new two-level network model, in which the first level is the whole unweighted-undirected network useful for macro-analyzing the industrial market structure while the second level is a local weighted-directed network capable of micro-analyzing the inter-firm rivalry in the market. It is believed that the relationship is determined by objective factors whereas the action is rather subjective, and the idea in this paper lies in that the objective relationship and the subjective action subjected to this relationship are being simultaneously considered but at deferent levels of the model which may be applicable to many real applications.
No abstract is provided for this article.
In this Letter, we propose a growing network model that can generate scale-free networks with a tunable community strength. The community strength, C, is directly measured by the ratio of the number of external edges to that of the internal ones; a smaller C corresponds to a stronger community structure. By using the Kuramoto model, we investigated the phase synchronization on this network and found an abnormal region ( C ⩽ 0.002 ), in which the network has even worse synchronizability than the unconnected case ( C = 0 ). On the other hand, the community effect will vanish when C exceeds 0.1. Between these two extreme regions, a stronger community structure will hinder global synchronization.
Aiming to develop a systematic approach for optimizing the structure of artificial higher order neural networks (HONN) for system modeling and function approximation, a new HONN topology, namely polynomial kernel networks, is proposed in this chapter. Structurally, the polynomial kernel network can be viewed as a three-layer feedforward neural network with a special polynomial activation function for the nodes in the hidden layer. The new network is equivalent to a HONN; however, due to the underlying connections with polynomial kernel support vector machines, the weights and the structure of the network can be determined simultaneously using structural risk minimization. The advantage of the topology of the polynomial kernel network and the use of a support vector kernel expansion paves the way to represent nonlinear functions or systems, and underpins some advanced analysis of the network performance. In this chapter, from the perspective of network complexity, both quadratic programming and linear programming based training of the polynomial kernel network are investigated. Request access from your librarian to read this chapter's full text.
Summary Further on the controllability of networked multiple‐input–multiple‐output systems, an efficient, necessary, and sufficient condition is derived, where the network topology is directed and weighted and the nodes are higher‐dimensional linear time‐invariant systems. The new condition is easier to verify, which explicitly shows the effects of the network topology, node‐system dynamics, external control inputs, and inner interactions on the controllability of the whole networked system. For networked multiple‐input–multiple‐output systems in several specific topologies, the corresponding conditions are expressed more precisely. The effectiveness of the conditions is demonstrated through several examples.
No abstract is provided for this article.
This paper presents several new chaos generators, switching piecewise-linear controllers, which can generate some new chaotic attractors with two or three merged basins of attraction from a given three-dimensional linear autonomous system within a wide range of parameter values. Based on this success, chaotic attractors with n merged basins of attraction are further generated using a formalized controller design methodology. Basic dynamical behaviors of the controlled chaotic system are then investigated via both theoretical analysis and numerical simulation. To that end, the underlying chaos-generation mechanism is further explored by analyzing the parameterization of the controlled system and the dynamics of the system orbits.
Network topology and nodal dynamics are two fundamental stones of adaptive networks. Detailed and accurate knowledge of these two ingredients is crucial for understanding the evolution and mechanism of adaptive networks. In this paper, by adopting the framework of the adaptive SIS model proposed by Gross et al. [Phys. Rev. Lett. 96, 208701 (2006)] and carefully utilizing the information of degree correlation of the network, we propose a link-based formalism for describing the system dynamics with high accuracy and subtle details. Several specific degree correlation measures are introduced to reveal the coevolution of network topology and system dynamics.
Combining the advantages of both integer-order and fractional-order complex chaotic systems, we propose a hybrid-order complex Lorenz system. We demonstrate its abundant chaotic characteristics, including symmetry and dissipation, fixed points and their stability and Lyapunov exponents, with 0–1 test. Then we show that, as the initial value, parameters and the order are varying, the system exhibits diverse dynamical behaviors, with fixed points, limit cycles and chaotic attractors. We further show that the system has coexisting attractors and parametric attractors. In addition, we find that the system generates different chaotic attractors as the system hybrid order varies, referred to as order attractors. Finally, we examine the dynamic transport of the hybrid-order complex Lorenz system and design a piecewise continuous controller to realize offset boosting control. By varying the initial value, parameters or orders, we realize the dynamic transport of the system. Our simulation results confirm the dynamic transport of the hybrid-order complex Lorenz system.
The important topic of multistability of continuous-and discrete-time neural network (NN) models has been investigated rather extensively. Concerning the design of associative memories, multistability of delayed hybrid NNs is studied in this paper with an emphasis on the impulse effects. Arising from the spiking phenomenon in biological networks, impulsive NNs provide an efficient model for synaptic interconnections among neurons. Using state-space decomposition, the coexistence of multiple equilibria of hybrid impulsive NNs is analyzed. Multistability criteria are then established regrading delayed hybrid impulsive neurodynamics, for which both the impulse effects on the convergence rate and the basins of attraction of the equilibria are discussed. Illustrative examples are given to verify the theoretical results and demonstrate an application to the design of associative memories. It is shown by an experimental example that delayed hybrid impulsive NNs have the advantages of high storage capacity and high fault tolerance when used for associative memories.
