For controlling an equilibrium of a nonlinear stochastic system, the problem of stabilization and synthesis with a required dispersion is studied. This problem is solved for the case where the feedback regulator uses noisy data. The new approach is based on an extension of the stochastic sensitivity synthesis method. Technically, this problem is reduced to the analysis of some quadratic matrix equations. A solution to the problem of minimizing the stochastic sensitivity is given. Details of such analysis are discussed for 2D and 3D nonlinear stochastic oscillators.
This paper studies the dynamics of multiagent systems with a multilayer structure, proposing a novel layered event-triggered scheme (LETS) for consensus. This LETS emphasizes on synchronous information transmission in the same layer but asynchronous message update between different layers, which differs from the existing centralized or distributed event-triggered schemes. Moreover, under the LETS, agents in different layers achieve asymptotical consensus eventually and the Zeno behavior is successfully eliminated. Furthermore, an algorithm is provided to avoid continuous event detection, verified by a numerical example.
A method is proposed for estimating the regulating time of PI-controlledAQM, which is closely related to the transient response time. It is shown that the regulating time of the PI-controlled AQM is explicitly related to its chosen parameter values and the network condition. Finally, comparisons show that the estimation is sufficiently accurate.
<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> In this paper, some existing perceptual encryption algorithms of MPEG videos are reviewed and some problems, especially security defects of two recently proposed MPEG-video perceptual encryption schemes, are pointed out. Then, a simpler and more effective design is suggested, which selectively encrypts fixed-length codewords in MPEG-video bit streams under the control of three perceptibility factors. The proposed design is actually an encryption configuration that can work with any stream cipher or block cipher. Compared with the previously-proposed schemes, the new design provides more useful features, such as strict size-preservation, on-the-fly encryption and multiple perceptibility, which make it possible to support more applications with different requirements. In addition, four different measures are suggested to provide better security against known/chosen-plaintext attacks. </para>
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.
This letter reports a study on some topological properties of chaos using a generalized competitive mode (GCM). The Lorenz system and the Chen system are used as examples for comparison. It is shown that for typical parameter values used in the two systems, the Lorenz attractor has one pair of GCMs in competition, while the Chen attractor has two pairs of GCMs in competition. This explains why the two attractors are topologically different, and furthermore indicates that the Chen attractor is more complex than the Lorenz attractor from the dynamics point of view.
Synchronization of coupled fast-spiking neurons with chemical synapses is studied in this paper. It is shown that by varying some key parameters such as the coupling strength and the decay rate of synapses, two coupled fast-spiking neurons can exhibit various firing synchronizations including periodic and chaotic motions. Different types of firing synchronizations are diagnosed by means of bifurcation diagrams and the largest Lyapunov exponent of the error dynamical system. However, with the synaptic delay considered, two coupled neurons can show different types of transitions of in-phase and anti-phase synchronizations and these transitions can be identified from the bifurcation diagrams and the variations of the phase errors of the coupled neurons. The revealed complicated synchronization modes effectively provide important guidelines to understanding collective behaviors of coupled neurons.
In this paper, we attempt to clarify an open problem related to a generalization of the snap-back repeller. Constructing a semi-conjugacy from the finite product of a transformation $f:\mathbb{R}^{n}\rightarrow \mathbb{R}^{n}$ on an invariant set $\Lambda$ to a sub-shift of the finite type on a $w$-symbolic space, we show that the corresponding transformation associated with the generalized snap-back repeller on $\mathbb{R}^{n}$ exhibits chaotic dynamics in the sense of having a positive topological entropy. The argument leading to this conclusion also shows that a certain kind of degenerate transformations, admitting a point in the unstable manifold of a repeller mapping back to the repeller, have positive topological entropies on the orbits of their invariant sets. Furthermore, we present two feasible sufficient conditions for obtaining an unstable manifold. Finally, we provide two illustrative examples to show that chaotic degenerate transformations are omnipresent.
A cellular neural network (CNN) is a massively parallel analog array processor capable of solving various complex processing problems by using specific templates that characterize the synaptic connections. The hardware implementation and applications of CNN have attracted a great deal of attention. Recently, memristors with nanometer-scale and variable gradual conductance have been exploited to make compact and programmable electric synapses. This paper proposes and studies a novel memristive CNN (Mt-CNN) with time-variant templates realized by memristor crossbar synaptic circuits. The template parameters are estimated analytically. The Mt-CNN provides a promising solution to hardware realization of real-time template updating processes, which can be used to effectively deal with various complicated problems of cascaded processing. Its effectiveness and advantages are demonstrated by practical examples of edge detection on noisy images.
No abstract is provided for this article.
This paper investigates the stability of the primal-dual algorithm for Internet congestion control in the presence of heterogeneous round-trip communication delays. Based on the geometric analysis of frequency response functions of the congestion control system, some sufficient conditions for the local asymptotic stability of the primal-dual algorithm are established. Compared to the existing references, the main contribution of this paper is that in addition to the new sufficient conditions it also obtains some scalability conditions under which the decentralized stability criteria hold for the primal-dual algorithm.
