2,312 publications from this institution
In recent years, there has been increasing interest and research work in the area of chaos control and synchronization. In particular, there has been some significant progress in the studies of identification, control and utilization of chaos by artificial intelligence technologies such as neural networks and fuzzy logic. This paper describes some of these results, with emphasis on the artificial neural networks approach with which the present author is currently involved.
In an analysis of a 2.92~fb$^{-1}$ data sample taken at 3.773~GeV with the BESIII detector operated at the BEPCII collider, we measure the absolute decay branching fractions to be $\mathcal B(D^0 \to K^-e^+ν_e)=(3.505\pm 0.014 \pm 0.033)\%$ and $\mathcal B(D^0 \to π^-e^+ν_e)=(0.295\pm 0.004\pm 0.003)\%$. From a study of the differential decay rates we obtain the products of hadronic form factor and the magnitude of the CKM matrix element $f_{+}^K(0)|V_{cs}|=0.7172\pm0.0025\pm 0.0035$ and $f_{+}^π(0)|V_{cd}|=0.1435\pm0.0018\pm 0.0009$. Combining these products with the values of $|V_{cs(d)}|$ from the SM constraint fit, we extract the hadronic form factors $f^K_+(0) = 0.7368\pm0.0026\pm 0.0036$ and $f^π_+(0) = 0.6372\pm0.0080\pm 0.0044$, and their ratio $f_+^π(0)/f_+^{K}(0)=0.8649\pm 0.0112\pm 0.0073$. These form factors and their ratio are used to test unquenched Lattice QCD calculations of the form factors and a light cone sum rule (LCSR) calculation of their ratio. The measured value of $f_+^{K(π)}(0) |V_{cs(d)}|$ and the lattice QCD value for $f^{K(π)}_+(0)$ are used to extract values of the CKM matrix elements of $|V_{cs}|=0.9601 \pm 0.0033 \pm 0.0047 \pm 0.0239$ and $|V_{cd}|=0.2155 \pm 0.0027 \pm 0.0014 \pm 0.0094$, where the third errors are due to the uncertainties in lattice QCD calculations of the form factors. Using the LCSR value for $f_+^π(0)/f_+^K(0)$, we determine the ratio $|V_{cd}|/|V_{cs}|=0.238\pm 0.004\pm 0.002\pm 0.011$, where the third error is from the uncertainty in the LCSR normalization. In addition, we measure form factor parameters for three different theoretical models that describe the weak hadronic charged currents for these two semileptonic decays. All of these measurements are the most precise to date.
Based on the well-known topological horseshoe theorem, a new rigorous computer-assisted proof for the existence of topological horseshoe in Chen's attractor is given. An appropriate Poincare section of Chen's attractor is chosen to obtain the corresponding Poincare map which is proved to be semi-conjugate to a 2-shift map. This implies that Chen's attractor has positive topological entropy, thus in this sense it is chaotic. The method used is somewhat simpler and more convenient than the classical Si'lnikov method.
No abstract is provided for this article.
This paper studies the effect of parameter mismatch on the impulsive synchronization of a class of coupled chaotic systems. A new definition for global quasisynchronization is introduced and used to analyze the synchronous behavior of coupled chaotic systems in the presence of parameter mismatch. Using the linear decomposition and comparison-system methods, a global synchronization error bound together with a sufficient condition is derived. Numerical simulations on the chaotic Chua’s circuit are presented to verify the theoretical results.
From the control theory point of view, it is reasonable to regard the TCP congestion control mechanism as a feedback control system. The TCP congestion control is complemented by the active queue management (AQM) scheme implemented in the routers, which could improve the effectiveness. Recently, an effective proportional-differential (PD) control algorithm has been proposed as a new AQM scheme for TCP congestion control. But it is still difficult to assign the parameter values of the PD-controller. This paper proposes a method for the design of an effective and stable PD-control AQM for routers in the Internet. For this purpose, a first-order plus time-delay TCP model is constructed, and a relay feedback method is employed to determine a set of stable parameter values of the model. From this set of values, and the requirements of the gain and phase margin, the specified parameter values of the PD-controller can be easily obtained by scanning the stable parameter set.
No abstract is provided for this article.
In this paper, a fuzzy logic controller is designed for back-driving a truck-trailer model into a prescribed parking lot along a suboptimal trajectory where the optimality is defined in terms of the natural parabolic driving paths that a truck-trailer travels. By applying fuzzy logic control techniques, a controller is developed with nine rules, which works well even without using a mathematical model of the truck-trailer system. As long as the states of the truck are measurable at each discrete-time step during the control process, this controller can drive the truck-trailer to follow any feasible trajectories, and to park successfully into the lot along a suboptimal trajectory. In addition to the design, controllability and stability of the control system are discussed, by using the small gain theorem for the truck-trailer system and the Lyapunov method for the single truck system. Simulation results are presented to demonstrate the accuracy and effectiveness of the new fuzzy logic controller, and to compare its control performance with other fuzzy logic controllers that were designed for the same purpose under the similar conditions without using any optimality criterion.
No abstract is provided for this article.
The statistical properties of the Lyapunov exponent of the chaotic generalized skew tent map is studied. Expressions of the mean and the variance of this Lyapunov exponent at each discrete time index are obtained. A sufficient condition for weakly mixing of the chaotic generalized skew tent map is derived, and the asymptotic distribution of its Lyapunov exponent is provided. Keywords: Central limit theoremDynamical systemInvariant measureLyapunov exponent Acknowledgments
This paper concerns the disturbance rejection problem of a linear complex dynamical network subject to external disturbances. A dynamical network is said to be robust to disturbance, if the H∞ norm of its transfer function matrix from the disturbance to the performance variable is satisfactorily small. It is shown that the disturbance rejection problem of a dynamical network can be solved by analysing the H∞ control problem of a set of independent systems whose dimensions are equal to that of a single node. A counter-intuitive result is that the disturbance rejection level of the whole network with a diffusive coupling will never be better than that of an isolated node. To improve this, local feedback injections are applied to a small fraction of the nodes in the network. Some criteria for possible performance improvement are derived in terms of linear matrix inequalities. It is further demonstrated via a simulation example that one can indeed improve the disturbance rejection level of the network by pinning the nodes with higher degrees than pinning those with lower degrees.
No abstract is provided for this article.
We offer a graphical analysis for detecting orbital oscillations in a neural network model. The approach taken is based on a frequency domain methodology, in which graphical techniques are used to capture some of the intrinsic dynamical features of periodic solutions in a nonlinear neural network system with time delays. The graphical analysis provides a correct detection of large-amplitude and multiple limit cycles existing in the network model.
This brief presents a simple technique using a sinusoidal parameter perturbation control input to drive a unified chaotic system to hyperchaotic. The original chaotic system is a three-dimensional autonomous system that has a broad spectrum of chaotic behaviors with the Lorenz and the Chen systems as two extremes of the spectrum. The control input is a simple sinusoidal function cos(/spl omega/t) with a constant parameter /spl omega/. The hyperchaotic system is not only demonstrated by computer simulations but also verified with bifurcation analysis and implemented via an electronic circuit.
No abstract is provided for this article.
In this paper, the problem of adaptive synchronization of uncertain coupled complex networks is investigated. Some controllers and adaptive laws are designed to ensure achieving synchronization of a general complex network model. In particular, synchronization of coupled stochastic networks subject to random perturbations is studied, with a referenced node introduced as the target node for synchronization. An example is simulated on delayed neural networks coupled in a small-world network topology, which demonstrates the feasibility and effectiveness of the proposed adaptive control method. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society
