Group Hybrid Coordination Control of Multi-Agent Systems With Time-Delays and Additive Noises

A new kind of group coordination control problemgroup hybrid coordination control is investigated in this paper.The group hybrid coordination control means that in a whole multi-agent system(MAS)that consists of two subgroups with communications between them,agents in the two subgroups achieve consensus and containment,respectively.For MASs with both time-delays and additive noises,two group control protocols are proposed to solve this problem for the containment-oriented case and consensus-oriented case,respectively.By developing a new analysis idea,some sufficient conditions and necessary conditions related to the communication intensity betw een the two subgroups are obtained for the following two types of group hybrid coordination behavior:1)Agents in one subgroup and in another subgroup achieve weak consensus and containment,respectively;2)Agents in one subgroup and in another subgroup achieve strong consensus and containment,respectively.It is revealed that the decay of the communication impact betw een the two subgroups is necessary for the consensus-oriented case.Finally,the validity of the group control results is verified by several simulation examples.

A Method for Reducing Ocean Wave-Induced Magnetic Noises in Shallow-Water MT Data Using a Complex Adaptive Filter

In shallow-water areas,the marine magnetotelluric(MT)method faces a challenge in the investigation of seabed conductivity structures due to electrical and magnetic noises induced by ocean waves,which seriously contaminate MT data.Ocean waves can affect electric and magnetic fields to different extents.In general,their influence on magnetic fields is considerably greater than that on electric fields.In this paper,a complex adaptive filter is adopted to reduce wave-induced magnetic noises in the frequency domain.The processing results of synthetic and measured MT data indicate that the proposed method can effectively reduce wave-induced magnetic noises and provide reliable apparent resistivity and phase data.

Effective Denoising Architecture for Handling Multiple Noises

Object detection,one of the core research topics in computer vision,is extensively used in various industrial activities.Although there have been many studies of daytime images where objects can be easily detected,there is relatively little research on nighttime images.In the case of nighttime,various types of noises,such as darkness,haze,and light blur,deteriorate image quality.Thus,an appropriate process for removing noise must precede to improve object detection performance.Although there are many studies on removing individual noise,only a few studies handle multiple noises simultaneously.In this paper,we pro-pose a convolutional denoising autoencoder(CDAE)-based architecture trained on various types of noises.We also present various composing modules for each noise to improve object detection performance for night images.Using the exclusively dark(ExDark)Image dataset,experimental results show that the Sequentialfiltering architecture showed superior mean average precision(mAP)compared to other architectures.

PARAMETER ESTIMATION FOR A CLASS OF STOCHASTIC DIFFERENTIAL EQUATIONS DRIVEN BY SMALL STABLE NOISES FROM DISCRETE OBSERVATIONS

We study the least squares estimation of drift parameters for a class of stochastic differential equations driven by small a-stable noises, observed at n regularly spaced time points ti = i/n, i = 1,...,n on [0, 1]. Under some regularity conditions, we obtain the consistency and the rate of convergence of the least squares estimator （LSE） when a small dispersion parameter ε→0 and n →∞ simultaneously. The asymptotic distribution of the LSE in our setting is shown to be stable, which is completely different from the classical cases where asymptotic distributions are normal.

Entropic noise induced stability and double entropic stochastic resonance induced by correlated noises

For the activated dynamics of a Brownian particle moving in a confined system with the presence of entropic barriers, this paper investigates a periodic driving and correlations between two noises. Within the two-state approximation, the explicit expressions of the mean first passage time （MFPT） and the spectral power amplification （SPA） axe obtained, respectively. Based on the numerical computations, it is found that： （i） The MFPT as a function of the noise intensity exhibits a maximum with the positive correlations between two noises （λ〉0）, this maximum for MFPT shows the characteristic of the entropic noise induced stability （ENIS） effect. The intensity A of correlations between two noises can enhance the ENIS effect. （ii） The SPA as a function of the noise intensity exhibits a double-peak by tuning the noise correlation intensity λ, i.e., the existence of a double-peak behaviour is the identifying characteristic of the double entropic stochastic resonance phenomenon.

Asymptotic Behavior of the Drift Coefficient Estimator of Stochastic Differential Equations Driven by Small Noises

The parametric estimation problem for diffusion processes with small white noise based on continuous time observations is well developed. However,in parametric inference,it is more realistic and interesting to consider asymptotic estimation for diffusion processes based on discrete observations. The least squares method is used to obtain the estimator of the drift parameter for stochastic differential equations( SDEs) driven by general Lévy noises when the process is observed discretely. Its strong consistency and the rate of convergence of the squares estimator are studied under some regularity conditions.

A Method of Eliminating Noises in Web Pages by Style Tree Model and Its Applications

A Web page typically contains many information blocks. Apart from the main content blocks, it usually has such blocks as navigation panels, copyright and privacy notices, and advertisements. We call these blocks the noisy blocks. The noises in Web pages can seriously harm Web data mining. To the question of climinating these noises, we intro duce a new tree structure, called Style Tree, and study an algorithm how to construct a site style tree. The Style Tree Model is employed to detect and climinate noises in any Web pages of the site. An information based measure to determine which element node is noisy is also constructed. In addition, the applications of this method are discussed in detail. Experimental results show that our noises climination technique is able to improve the mining results significantly. Key words noises climination - DOM tree - style tree - Web mining CLC number TP 339 Foundation item: Supported by the National Natural Science Foundation of China (60003013)Biography: ZHAN Cheng-li (1979-), male, Master candidate, research direction: Intelligent Information System.

Stochastic resonance for a metapopulation system driven by multiplicative and additive colored noises

We investigate the stochastic resonance （SR） phenomenon induced by the periodic signal in a metapopulation system with colored noises. The analytical expression of signal-to-noise is derived in the adiabatic limit. By numerical calculation, the effects of the addictive noise intensity, the multiplicative noise intensity and two noise self-correlation times on SNR are respectively discussed. It shows that： （i） in the case that the addictive noise intensity M takes a small value, a SR phenomenon for the curve of SNR appears; however, when M takes a large value, SNR turns into a monotonic function on the multiplicative noise intensity Q. （ii） The resonance peaks in the plots of the multiplicative noise intensity Q versus its self-correlation time Vl and the addictive noise intensity M versus its self-correlation time ~2 translate in parallel. Mean- while, a parallel translation also appears in the plots of vl versus Q and v2 versus M. （iii） The interactive effects between self-correlation times Vl and v2 are opposite.

Stochastic bounded consensus tracking of leader-follower multi-agent systems with measurement noises and sampled-data

This paper is concerned with the stochastic bounded consensus tracking problems of leader-follower multi-agent systems, where the control input of an agent can only use the information measured at the sampling instants from its neighbours or the virtual leader with a time-varying reference state, and the measurements are corrupted by random noises. The probability limit theory and the algebra graph theory are employed to derive the necessary and sufficient conditions guaranteeing the mean square bounded consensus tracking. It is shown that the maximum allowable upper boundary of the sampling period simultaneously depends on the constant feedback gains and the network topology. Furthermore, the effects of the sampling period on the tracking performance are analysed. It turns out that from the view point of the sampling period, there is a trade-off between the tracking speed and the static tracking error. Simulations are provided to demonstrate the effectiveness of the theoretical results.

Statistical fluctuations in a saturation laser model with correlated noises

We study the effects of correlations between quantum and pump noises on fluctuations of the laser intensity in a saturation laser model. An approximative Fokker-Planck equation and analytic expressions of the steady-state probability distribution function （SPD） of the laser system are derived. Based on the SPD, the normalized mean, the normalized variance, and the normalized skewness of the steady-state laser intensity are calculated numerically. The results indicate that （i） the correlation strength A of correlated noises always enhances the fluctuation of laser intensity; （ii） the correlation time v of correlated noises strengthens the fluctuation of laser intensity for the below-threshold case but τ weakens it for the above-threshold case.

Stochastic resonance in an asymmetric bistable system driven by coloured noises

The phenomenon of stochastic resonance is investigated in an asymmetric bistable system with coloured noises. The approximate Fokker Planck equation is derived based on the Novikov theorem and the Fox approach. By applying the two-state theory, the expression of the signal-to-noise ratio is obtained in the adiabatic limit. The effects of the noise parameters on signal-to-ratio are discussed. It is found that the stochastic resonance phenomena appear in most cases and disappear in some special cases.

Stochastic resonance in the gene transcriptional regulatory system subjected to noises

We have investigated in the adiabatic limit the phenomenon of stochastic resonance in the gene transcriptional regulatory system subjected to an additive noise, a multiplicative noise, and a weakly periodic signal. Using the general two-state approach for the asymmetry system, the analytic expression of signal-to-noise ratio is obtained. The effects of the additive noise intensity a, the multiplicative noise intensity D and the amplitude of input periodic signal A on the signal-to-noise ratio are analysed by numerical calculation. It is found that the existence of a maximum in the RSNR a and RSNR D plots is the identifying characteristic of the stochastic resonance phenomenon in the weakened noise intensity region. The stochastic resonance phenomena are restrained with increasing a and D, and enhanced with increasing A.

Stochastic bounded consensus tracking of leader-follower multi-agent systems with measurement noises based on sampled data with general sampling delay

In this paper we provide a unified framework for consensus tracking of leader-follower multi-agent systems with measurement noises based on sampled data with a general sampling delay. First, a stochastic bounded consensus tracking protocol based on sampled data with a general sampling delay is presented by employing the delay decomposition technique. Then, necessary and sufficient conditions are derived for guaranteeing leader-follower multi-agent systems with measurement noises and a time-varying reference state to achieve mean square bounded consensus tracking. The obtained results cover no sampling delay, a small sampling delay and a large sampling delay as three special cases. Last, simulations are provided to demonstrate the effectiveness of the theoretical results.

Escape of Brownian Particles in the System Driven by Correlated White Noises:Comparison of Three Models

The relative escape rate (RER) in the bistable sawtooth system driven by correlated white noises was studied in the case of linear multiplicative noise coefficient.And we compare the RER of linear multiplicative noise coefficient with the RER of the piecewise constant multiplicative noise coefficient in the bistable sawtooth system,finally compare the RER of the bistable sawtooth potential with the RER of the quartic potential in the case of linear multiplicative noise coefficient.It can be seen that the form of the multiplicative noise coefficient and the nonlinearity of potential have an important influence on the resonant activation and the suppression of the RER.The resonant activation of the RER vanishes in the negative correlation when the multiplicative noise coefficient was shifted from piecewise constant to linear function in the bistable sawtooth potential.The suppression of the RER disappears in the negative correlation when thelinear potential is converted into nonlinear one.

Current transport and mass separation for an asymmetric fluctuation system with correlated noises

We discuss the transport of an underdamped particle driven by an external fluctuation force in a spatially periodic asymmetric potential with correlated noises. The corresponding mathematical model is established. The movement of the steady current of an underdamped particle is presented by the method of the numerical simulation. It is indicated that the value of the current may be negative, zero, or positive. The external fluctuation force and correlated noises can effect the current direction. Under the appropriate parameters, the correlated noises intensity may even raise a reversal of the current. Besides, we have noticed a phenomenon that particles with different weight have different directions during movement by the impact of the correlated noises and external fluctuation force. Therefore, the Brownian particles can be effectively separated according to their masses.

Effects of intrinsic and extrinsic noises on transposons kinetics

The absolute concentration robustness （ACR） steady state of a biochemical system can protect against changing a large concentration of the system＇s components. In this paper, a minimal model of autonomous-nonautonomous transposons driven by intrinsic and extrinsic noises is investigated. The effects of intrinsic and extrinsic noises on ACR steady state of the transposons kinetics are studied by numerical simulations. It is found that the predator-prey-like oscillations around the ACR steady state are induced by the intrinsic or extrinsic noises. Comparing with the case of intrinsic noises, the extrinsic noises can inhibit the amplitude of oscillations of transposon kinetics. To characterize the predator-prey-like oscillations, we calculate the probability distributions and the normalized correlation functions of a system in the stability domain. With the increasing of noise intensity, the peak of the probability distribution is shifted from the ACR steady state to the trivial steady state. The normalized autocorrelation and cross-correlation functions indicate that the state of the predator-prey oscillator is transmitted to 50 successive generations at least.

Mean-square composite-rotating consensus of second-order systems with communication noises

We study the mean-square composite-rotating consensus problem of second-order multi-agent systems with communication noises, where all agents rotate around a common center and the center of rotation spins around a fixed point simultaneously. Firstly, a time-varying consensus gain is introduced to attenuate to the effect of communication noises. Secondly, sufficient conditions are obtained for achieving the mean-square composite-rotating consensus. Finally, simulations are provided to demonstrate the effectiveness of the proposed algorithm.

ELEMENTARY BIFURCATIONS FOR A SIMPLE DYNAMICAL SYSTEM UNDER NON-GAUSSIAN LVY NOISES

Nonlinear dynamical systems are sometimes under the influence of random fluctuations. It is desirable to examine possible bifurcations for stochastic dynamical systems when a parameter varies.A computational analysis is conducted to investigate bifurcations of a simple dynamical system under non-Gaussian a-stable Levy motions, by examining the changes in stationary probability density functions for the solution orbits of this stochastic system. The stationary probability density functions are obtained by solving a nonlocal Fokker-Planck equation numerically. This allows numerically investigating phenomenological bifurcation, or P-bifurcation, for stochastic differential equations with non-Gaussian Levy noises.

Simulation study of the two-time intensity correlation function of a two-mode laser system with both pump and quantum noises

This paper investigates the two-time intensity correlation function of a two-mode ring laser system subjected to both pump and quantum noises by stochastic simulation. It finds that the decay rate of the intensity correlation function of one mode gets faster with decreasing values of relevant parameters, i.e., the coupling constant ξ, the cross-correlation coefficient A, the difference of the pump parameters Aa and the pump parameter al; however, its variations get complex in the other mode when relevant parameters are changed. The investigating results also show that the effects of the mode competition on intensity correlation function are obvious.

GEKF,GUKF and GGPF based prediction of chaotic time-series with additive and multiplicative noises

On the assumption that random interruptions in the observation process are modelled by a sequence of independent Bernoulli random variables, this paper generalize the extended Kalman filtering （EKF）, the unscented Kalman filtering （UKF） and the Gaussian particle filtering （GPF） to the case in which there is a positive probability that the observation in each time consists of noise alone and does not contain the chaotic signal （These generalized novel algorithms are referred to as GEKF, GUKF and GGPF correspondingly in this paper）. Using weights and network output of neural networks to constitute state equation and observation equation for chaotic time-series prediction to obtain the linear system state transition equation with continuous update scheme in an online fashion, and the prediction results of chaotic time series represented by the predicted observation value, these proposed novel algorithms are applied to the prediction of Mackey-Glass time-series with additive and multiplicative noises. Simulation results prove that the GGPF provides a relatively better prediction performance in comparison with GEKF and GUKF.