2,312 publications from this institution
A large number of real-world complex networks or their subnetworks possess excellent dynamical properties such as high dynamic synchronizability, optimal controllability, strong resistance to attacks, fast information transmission capability, and natural emergence of cooperation in evolutionary games, etc., but existing network models are unable to well represent these intrinsic features and ubiquitous phenomena. this paper examines an optimal homogeneous network model which can well describe at least one of such optimal dynamical behaviors-the best possible synchronizability.
Establishing and operating an oilfield surface pipeline system involves a large capital expenditure, which includes pipeline cost, power consumption cost, and thermal energy cost, etc. In this process, there exists a vast amount of fuzzy information, in both the objective and the constraint functions for an optimal design. System design based on such a huge volume of fuzzy information about multi-objectives and multi-constraints cannot be well handled by conventional mathematical tools. In this paper, a mathematical model from the fuzzy optimization approach is formulated for such oilfield surface pipeline systems. Both the multi-objective and multi-constraint functions are defined in a feasible fuzzy domain within the design parameters space. An optimal solution is selected from this domain for the design, for which the degree of expert satisfaction reaches the maximum. The methodology presented in this paper offers a new approach and a significant improvement of cost savings in the establishment and operation of an oilfield surface pipeline network system, beneficial for the oil industry.
Prediction of period-doubling bifurcation is accomplished very accurately by using higher-order Harmonic Balance Approximations (HBAs) and quasi-analytical monodromy-matrix evaluation. Approximation error analysis is carried out for the computation. An accurate detection of first period-doubling bifurcation in Chua's circuit is demonstrated.
Despite the prevalence of synchronization analysis on complex dynamical networks, little attention was paid to the problem of estimating their regions of attraction. This paper addresses the issue of estimating the region of attraction of an equilibrium point of a complex dynamical network, and briefly analyzes the network stability. A sufficient condition and a necessary condition are first established for the asymptotical stability of the network equilibrium point. Then, a general technique for region-of-attraction estimation is developed by combining the network structure and the node dynamics. In order to avoid the troublesome parameter selection in general region-of-attraction estimation, second-order estimation is solved under a mild additional condition. Examples are provided to verify the theoretical estimations.
In this paper, by combining smart neural design with a recently proposed ISS-modular neural control approach, we present a smart neural control scheme for general (non-affine) pure-feedback systems. Although the neural controller in achieves a semi-global result for general (non-affine) pure-feedback systems, it is by nature a high-order dynamic controller, which cannot be reduced in general due to its need of simultaneous adaptation of a large number of neural weights. To overcome this problem, in this paper we develop a smart neural controller, which on the contrary is a static and low-order controller, hence more computationally feasible in practical design and implementation. To improve the NN generalization ability, which plays an important role in our smart neural control scheme, chaotic reference signals are employed in the training phase of the scheme, where the complex chaotic signals offer much richer information for NN learning due to the ergodicity of chaos. Since pure-feedback system represents a very large class of nonlinear systems, the smart neural control scheme is expected to be useful for a wide variety of industrial applications.
Based on the S̆ilnikov criterion, a simple quadratic chaotic system is constructed, which has a single equilibrium point. The formation mechanism shows that this chaotic system has Smale horseshoes (homoclinic chaos), and numerical simulation demonstrates that there is a route to chaos through period-doubling bifurcations. In particular, the method of finding chaotic systems can be used to construct rather arbitrary chaotic attractors of even number of scrolls and arbitrary odd number of scrolls.
Bifurcations of a class of one-dimensional reaction–diffusion equations of the form u″+μu-u k =0, where μ is a parameter, 2≤k∈Z + , with boundary value condition u(0)=u(π)=0, are investigated. Using the singularity theory based on the Liapunov–Schmidt reduction, some characterization results are obtained.
A finite-time controller is designed for a class of nonlinear systems subject to sector nonlinear inputs. A novel and simple approach is suggested based on the finite-time control principle. The designed sliding-mode controller can drive a chaotic system to track a smooth target signal in a finite time. The chaotic Duffing–Holmes oscillator is used for verification and demonstration.
For a shallow water model with Coriolis effect, by applying the methodologies of dynamical systems and singular traveling wave theory developed by Li and Chen [2007] to its traveling wave system, under different parameter conditions, all possible bounded solutions (solitary wave solution, pseudo-peakon and periodic peakons as well as compactons) are obtained. Some exact explicit parametric representations are presented.
For a network, knowledge of its Laplacian eigenvalues is central to understanding its structure and dynamics. In this paper, we study the Laplacian spectra of a family of Koch networks with scale-free and small-world properties. We derive some recursive relations between the Laplacian characteristic polynomials of Koch networks and their subgraphs at different iterations. Based on the obtained recurrence relations, we determine explicitly the product of all nonzero Laplacian eigenvalues, as well as the sum of the reciprocals of these eigenvalues. Then, using these results, we further evaluate the number of spanning trees, Kirchhoff index, global mean first-passage time and average path length of the family of Koch networks. Finally, we determine the number of spanning forests under certain conditions. We expect that our method can be adapted to other types of self-similar networks.
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