Influence Of Stochastic Disturbances Research Articles

Predefined-Time Synchronization of Stochastic Memristor-Based Bidirectional Associative Memory Neural Networks With Time-Varying Delays

In this article, we consider the predefined-time synchronization problem of stochastic memristor-based bidirectional associative memory neural networks (MBAMNNs). First, considering the influence of stochastic disturbance, we propose two new predefined-time theorems. Second, combined with the predefined-time theorem proposed in this article, we design a feedback controller to realize the predefined-time synchronization of MBAMNNs. Then, the sufficient conditions for the predefined-time synchronization of MBAMNNs are obtained based on Ito’s formula and Lyapunov theorem. Finally, the effectiveness of the theorems is verified by numerical simulations.

Event-triggered annular finite-time [formula omitted] filtering for stochastic network systems

The event-triggered annular finite-time H∞ filtering problem is addressed for stochastic network systems in this paper. The influence of stochastic disturbance is considered in the whole framework of filter design. A zero-order-holder (ZOH) is introduced to maintain and send the delayed output signals for filter updating. By resorting to a novel delay-dependent Lyapunov-Krasovskii functional (LKF) and LMI technique, the stochastic annular finite-time boundedness of the filtering error system is analyzed. Furthermore, the H∞ filter design under the event-triggered mechanism is established in the sense of a prescribed H∞ performance level. Finally, two numerical examples are realized to illustrate the validity of the proposed results.

Cluster-Delay Mean Square Consensus of Stochastic Multi-Agent Systems with Impulse Time Windows.

This paper investigates the cluster-delay mean square consensus problem of a class of first-order nonlinear stochastic multi-agent systems with impulse time windows. Specifically, on the one hand, we have applied a discrete control mechanism (i.e., impulsive control) into the system instead of a continuous one, which has the advantages of low control cost, high convergence speed; on the other hand, we considered the existence of impulse time windows when modeling the system, that is, a single impulse appears randomly within a time window rather than an ideal fixed position. In addition, this paper also considers the influence of stochastic disturbances caused by fluctuations in the external environment. Then, based on algebraic graph theory and Lyapunov stability theory, some sufficiency conditions that the system must meet to reach the consensus state are given. Finally, we designed a simulation example to verify the feasibility of the obtained results.

Stable bounded value analysis of disturbance in stochastic linear power systems

Stochastic disturbances play a profound problem in the power system, which have an important impact on the stability of the power system. The paper proposes the stability analysis of stochastic disturbance bounded value of linear power system, and presents that the stability of power system has bounded value under stochastic disturbance and additional disturbance, and gives the analysis process in combination with stochastic differentiation. The equation theory proposes a numerical solution based on mean stability to calculate the boundedness of infinite systems under the influence of stochastic disturbance and additional disturbance. The results show that the system has bounded value stability under the disturbance.

Stochastic Control for Intra-Region Probability Maximization of Multi-Machine Power Systems Based on the Quasi-Generalized Hamiltonian Theory

With the penetration of renewable generation, electric vehicles and other random factors in power systems, the stochastic disturbances are increasing significantly, which are necessary to be handled for guarantying the security of systems. A novel stochastic optimal control strategy is proposed in this paper to reduce the impact of such stochastic continuous disturbances on power systems. The proposed method is effective in solving the problems caused by the stochastic continuous disturbances and has two significant advantages. First, a simplified and effective solution is proposed to analyze the system influenced by the stochastic disturbances. Second, a novel optimal control strategy is proposed in this paper to effectively reduce the impact of stochastic continuous disturbances. To be specific, a novel excitation controlled power systems model with stochastic disturbances is built in the quasi-generalized Hamiltonian form, which is further simplified into a lower-dimension model through the stochastic averaging method. Based on this Itô equation, a novel optimal control strategy to achieve the intra-region probability maximization is established for power systems by using the dynamic programming method. Finally, the intra-region probability increases in controlled systems, which confirms the effectiveness of the proposed control strategy. The proposed control method has advantages on controlling the fluctuation of system state variables within a desired region under the influence of stochastic disturbances, which means improving the security of stochastic systems. With more stochasticity in the future, the proposed control method based on the stochastic theory will play a novel way to relieve the impact of stochastic disturbances.

STUDYING THE INFLUENCE OF STOCHASTIC DISTURBANCES ON THE CRYSTALLIZATION OF AMIDOSULFONIC ACID

This paper introduces a method that simulates the negative impact that the stochastic disturbances may have on the desired operation for the reactors in chemical engineering. The results obtained in our research, with emphasis on the precipitation of poly-dispersive heterogeneous particles from liquid phase containing amidosulfonic acid, indicate that chaotic changes of input parameters such as volumetric flow rate Vin (1) , oversaturation Sc , … caused to seriously reduce effect of the crystallization process. Therefore suppressing the influence of stochastic disturbances on a process is the important objective for an equipment system operating with poly-dispersive heterogeneous phases.

Low dimensional homeochaos in coevolving host–parasitoid dimorphic populations: Extinction thresholds under local noise

A discrete time model describing the population dynamics of coevolution between host and parasitoid haploid populations with a dimorphic matching allele coupling is investigated under both determinism and stochastic population disturbances. The role of the properties of the attractors governing the survival of both populations is analyzed considering equal mutation rates and focusing on host and parasitoid growth rates involving chaos. The purely deterministic model reveals a wide range of ordered and chaotic Red Queen dynamics causing cyclic and aperiodic fluctuations of haplotypes within each species. A Ruelle–Takens–Newhouse route to chaos is identified by increasing both host and parasitoid growth rates. From the bifurcation diagram structure and from numerical stability analysis, two different types of chaotic sets are roughly differentiated according to their size in phase space and to their largest Lyapunov exponent: the Confined and Expanded attractors. Under the presence of local population noise, these two types of attractors have a crucial role in the survival of both coevolving populations. The chaotic confined attractors, which have a low largest positive Lyapunov exponent, are shown to involve a very low extinction probability under the influence of local population noise. On the contrary, the expanded chaotic sets (with a higher largest positive Lyapunov exponent) involve higher host and parasitoid extinction probabilities under the presence of noise. The asynchronies between haplotypes in the chaotic regime combined with low dimensional homeochaos tied to the confined attractors is suggested to reinforce the long-term persistence of these coevolving populations under the influence of stochastic disturbances. These ideas are also discussed in the framework of spatially-distributed host–parasitoid populations.

Discrete-time movement model of a group of trains on a rail line with stochastic disturbance

This paper presents a discrete-time model to describe the movements of a group of trains, in which some operational strategies, including traction operation, braking operation and impact of stochastic disturbance, are defined. To show the dynamic characteristics of train traffic flow with stochastic disturbance, some numerical experiments on a railway line are simulated. The computational results show that the discrete-time movement model can well describe the movements of trains on a rail line with the moving-block signalling system. Comparing with the results of no disturbance, it finds that the traffic capacity of the rail line will decrease with the influence of stochastic disturbance. Additionally, the delays incurred by stochastic disturbance can be propagated to the subsequent trains, and then prolong their traversing time on the rail line. It can provide auxiliary information for rescheduling trains when the stochastic disturbance occurs on the railway.

Influence of stochastic disturbances on the quasi-wavelet solution of the convection–diffusion equation

A quasi-wavelet numerical method (QWNM) is introduced for solving the convection–diffusion equation (CDE). The results manifest that the calculated bandwidth has an extremum. When the bandwidth takes the value of the extremum, the accuracy of the solution for the CDE by using the QWNM is relatively high and better than that by using the up-wind scheme. Under the condition of stochastic boundary disturbances of different amplitudes, the results of the QWNM are a little worse than those of the up-wind scheme when the integral time is longer. However, when stochastic boundary disturbances of equal amplitudes occur, the solutions of the equation by using the QWNM and the up-wind scheme can be identical if the bandwidth takes an integer greater than or equal to 20. When the parameter is stochastically disturbed, the root-mean-square error of the quasi-wavelet solution of the equation is smaller than that of the up-wind scheme solution if the bandwidth is 10. When the initial values are stochastically disturbed and the bandwidth equals 10, the accuracy of the quasi-wavelet solution is relatively high and better than that of the up-wind scheme solution.

Optimal inventory-production control in discrete manufacturing systems

The problem of inventory-production control in discrete manufacturing systems in the presence of stochastic disturbances and measurement noise is considered. The proposed control algorithms are based on the internal model control principle. In such a case the closed-loop system has a selective invariance properties and the influence of stochastic disturbances may be sufficienüy decreased. The problems of delay modeling, identification and compensation in inventory control systems are also considered. The generalized predictor-based delay compensating controller as well as design procedure is proposed and comparison with known methods has been performed. The new type of random delay model is developed using Hidden Markov Model technique. It is shown that such an approach leads to the simple varying delay detection and random delay identification algorithms based on ordinary recurrent estimation technique.