Linear impulsive control system with impulse time windows

2016 ◽  
Vol 23 (1) ◽  
pp. 111-118 ◽  
Author(s):  
Yuming Feng ◽  
Junzhi Yu ◽  
Chuandong Li ◽  
Tingwen Huang ◽  
Hangjun Che
Keyword(s):  
Impulsive Control ◽  
Time Window ◽  
Time Windows ◽  
Practical Importance ◽  
Fixed Time ◽  
Small Time ◽  
Time Interval ◽  
Impulsive Systems ◽  
Impulse Time Windows ◽  
System States

We formulate the linear impulsive control systems with impulse time windows. Different from the most impulsive systems where the impulses occur at fixed time or when the system states hit a certain hyperplane, the impulse time in the presented systems might be uncertain, but limited to a small time interval, i.e. a time window. Compared with the existing impulsive systems, the systems with impulse time windows is of practical importance. We then study the asymptotic stability of the case of linear systems and obtain several stability criteria. Numerical examples are given to verify the effectiveness of the theoretical results.

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

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 1033
Author(s):  
Huan Luo ◽  
Yinhe Wang ◽  
Ruidian Zhan ◽  
Xuexi Zhang ◽  
Haoxiang Wen ◽  
...  
Keyword(s):  
Impulsive Control ◽  
Time Window ◽  
Time Windows ◽  
Discrete Control ◽  
Mean Square ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Impulse Time Windows ◽  
Multi Agent ◽  
Mean Square Consensus

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.

Parameters of Spike Trains Observed in a Short Time Window

2008 ◽  
Vol 20 (5) ◽  
pp. 1325-1343 ◽  
Author(s):  
Zbyněk Pawlas ◽  
Lev B. Klebanov ◽  
Martin Prokop ◽  
Petr Lansky
Keyword(s):  
Coefficient Of Variation ◽  
Point Processes ◽  
Interspike Interval ◽  
Time Window ◽  
Time Windows ◽  
Time Interval ◽  
Short Time Interval ◽  
Target Neuron ◽  
Short Time Window ◽  
Short Time

We study the estimation of statistical moments of interspike intervals based on observation of spike counts in many independent short time windows. This scenario corresponds to the situation in which a target neuron occurs. It receives information from many neurons and has to respond within a short time interval. The precision of the estimation procedures is examined. As the model for neuronal activity, two examples of stationary point processes are considered: renewal process and doubly stochastic Poisson process. Both moment and maximum likelihood estimators are investigated. Not only the mean but also the coefficient of variation is estimated. In accordance with our expectations, numerical studies confirm that the estimation of mean interspike interval is more reliable than the estimation of coefficient of variation. The error of estimation increases with increasing mean interspike interval, which is equivalent to decreasing the size of window (less events are observed in a window) and with decreasing the number of neurons (lower number of windows).

scholarly journals Trade-offs between driving nodes and time-to-control in complex networks

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Sérgio Pequito ◽  
Victor M. Preciado ◽  
Albert-László Barabási ◽  
George J. Pappas
Keyword(s):  
Complex Networks ◽  
Time Window ◽  
Empirical Studies ◽  
Practical Importance ◽  
Network Models ◽  
Small Time ◽  
Synthetic Network ◽  
Trade Offs ◽  
Minimum Number ◽  
Time Required

Abstract Recent advances in control theory provide us with efficient tools to determine the minimum number of driving (or driven) nodes to steer a complex network towards a desired state. Furthermore, we often need to do it within a given time window, so it is of practical importance to understand the trade-offs between the minimum number of driving/driven nodes and the minimum time required to reach a desired state. Therefore, we introduce the notion of actuation spectrum to capture such trade-offs, which we used to find that in many complex networks only a small fraction of driving (or driven) nodes is required to steer the network to a desired state within a relatively small time window. Furthermore, our empirical studies reveal that, even though synthetic network models are designed to present structural properties similar to those observed in real networks, their actuation spectra can be dramatically different. Thus, it supports the need to develop new synthetic network models able to replicate controllability properties of real-world networks.

Stability analysis of hybrid neural networks with impulsive time window

2016 ◽  
Vol 10 (01) ◽  
pp. 1750011 ◽  
Author(s):  
Xin Wang ◽  
Hui Wang ◽  
Chuandong Li ◽  
Tingwen Huang
Keyword(s):  
Neural Networks ◽  
Lyapunov Functions ◽  
Impulsive Control ◽  
Time Window ◽  
Control Strategies ◽  
Urgent Problem ◽  
Impulsive Systems ◽  
Random Manner ◽  
New Challenges ◽  
Hybrid Neural Networks

The urgent problem with impulsive moments cannot be determined in advance brings new challenges beyond the conventional impulsive systems theory. In order to solve this problem, in this paper, a novel class of system with impulsive time window is proposed. Different from the conventional impulsive control strategies, the main characteristic of the impulsive time window is that impulse occurs in a random manner. Moreover, for the importance of the hybrid neural networks, using switching Lyapunov functions and a generalized Hanlanay inequality, some general criteria for asymptotic and exponential stability of the hybrid neural networks with impulsive time window are established. Finally, some simulations are provided to further illustrate the effectiveness of the results.

scholarly journals Detecting Dynamic States of Temporal Networks Using Connection Series Tensors

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Shun Cao ◽  
Hiroki Sayama
Keyword(s):  
Temporal Dynamics ◽  
Time Window ◽  
Time Windows ◽  
Social Contact ◽  
Network Data ◽  
Temporal Network ◽  
Temporal Networks ◽  
Network Aggregation ◽  
System States ◽  
Dynamic States

Many temporal networks exhibit multiple system states, such as weekday and weekend patterns in social contact networks. The detection of such distinct states in temporal network data has recently been studied as it helps reveal underlying dynamical processes. A commonly used method is network aggregation over a time window, which aggregates a subsequence of multiple network snapshots into one static network. This method, however, necessarily discards temporal dynamics within the time window. Here we propose a new method for detecting dynamic states in temporal networks using connection series (i.e., time series of connection status) between nodes. Our method consists of the construction of connection series tensors over nonoverlapping time windows, similarity measurement between these tensors, and community detection in the similarity network of those time windows. Experiments with empirical temporal network data demonstrated that our method outperformed the conventional approach using simple network aggregation in revealing interpretable system states. In addition, our method allows users to analyze hierarchical temporal structures and to uncover dynamic states at different spatial/temporal resolutions.

scholarly journals LAMN property for the drift and volatility parameters of a sde driven by a stable Lévy process

2019 ◽  
Vol 23 ◽  
pp. 136-175 ◽  
Author(s):  
Emmanuelle Clément ◽  
Arnaud Gloter ◽  
Huong Nguyen
Keyword(s):  
Stable Process ◽  
Transition Density ◽  
Fixed Time ◽  
Small Time ◽  
Time Interval ◽  
Lévy Measure ◽  
Stable Lévy Process ◽  
Continuous Time Process ◽  
Asymptotic Mixed Normality ◽  
Drift Coefficient

This work focuses on the local asymptotic mixed normality (LAMN) property from high frequency observations, of a continuous time process solution of a stochastic differential equation driven by a truncated α-stable process with index α ∈ (0, 2). The process is observed on the fixed time interval [0,1] and the parameters appear in both the drift coefficient and scale coefficient. This extends the results of Clément and Gloter [Stoch. Process. Appl. 125 (2015) 2316–2352] where the index α ∈ (1, 2) and the parameter appears only in the drift coefficient. We compute the asymptotic Fisher information and find that the rate in the LAMN property depends on the behavior of the Lévy measure near zero. The proof relies on the small time asymptotic behavior of the transition density of the process obtained in Clément et al. [Preprint HAL-01410989v2 (2017)].

scholarly journals A Modified Asynchronous Transfer Mode Fuzzy Policer

2002 ◽  
Vol 7 (1) ◽  
pp. 137
Author(s):  
M.R.M. Rizk ◽  
H. Rashwan ◽  
A. Abdel Aziz
Keyword(s):  
Time Window ◽  
Asynchronous Transfer Mode ◽  
Time Windows ◽  
Time Interval ◽  
Multiple Channels ◽  
Transfer Mode ◽  
Good Improvement

A Modified Fuzzy policer for Asynchronous Transfer Mode is introduced. In a preceding fuzzy policer model the time window (time interval where ATM cells are accepted in the policer) is not synchronized with the source activity. In the proposed one, the time windows are not consecutive but are triggered by the first arriving cell. The modified policer gives good improvement to the selectivity, and minimizes the congestion over the path. This improvement can be significant for multiple channels.  

scholarly journals Efficient Delivery Services Sharing with Time Windows

2020 ◽  
Vol 10 (21) ◽  
pp. 7431
Author(s):  
Wanyuan Wang ◽  
Hansi Tao ◽  
Yichuan Jiang
Keyword(s):  
Local Search ◽  
Time Window ◽  
Time Windows ◽  
Fixed Time ◽  
The Other ◽  
System Throughput ◽  
Delivery Service ◽  
Other Hand ◽  
Service Routing ◽  
Off Time

Delivery service sharing (DSS) has made an important contribution in the optimization of daily order delivery applications. Existing DSS algorithms introduce two major limitations. First, due to computational reasons, most DSS algorithms focus on the fixed pickup/drop-off time scenario, which is inconvenient for real-world scenarios where customers can choose the pickup/drop-off time flexibly. On the other hand, to address the intractable DSS with the flexible time windows (DSS-Fle), local search-based heuristics are widely employed; however, they have no theoretical results on the advantage of order sharing. Against this background, this paper designs a novel algorithm for DSS-Fle, which is efficient on both time complexity and system throughput. Inspired by the efficiency of shareability network on the delivery service routing (DSR) variant where orders cannot be shared and have the fixed time window, we first consider the variant of DSR with flexible time windows (DSR-Fle). For DSR-Fle, the order’s flexible time windows are split into multiple virtual fixed time windows, one of which is chosen by the shareability network as the order’s service time. On the other hand, inspired by efficiency of local search heuristics, we further consider the variant of DSS with fixed time window (DSS-Fix). For DSS-Fix, the beneficial sharing orders are searched and inserted to the shareability network. Finally, combining the spitting mechanism proposed in DSR-Fle and the inserting mechanism proposed in DSS-Fix together, an efficient algorithm is proposed for DSS-Fle. Simulation results show that the proposed DSS-Fle variant algorithm can scale to city-scale scenarios with thousands of regions, orders and couriers, and has the significant advantage on improving system throughput.

scholarly journals Solving Online Threat Screening Games using Constrained Action Space Reinforcement Learning

2020 ◽  
Vol 34 (02) ◽  
pp. 2226-2235
Author(s):  
Sanket Shah ◽  
Sinha Arunesh ◽  
Varakantham Pradeep ◽  
Perrault Andrew ◽  
Tambe Milind
Keyword(s):  
Reinforcement Learning ◽  
Large Scale ◽  
Stackelberg Game ◽  
Time Window ◽  
Time Windows ◽  
Inequality Constraints ◽  
Wait Time ◽  
Limited Resource ◽  
Fixed Time ◽  
Action Space

Large-scale screening for potential threats with limited resources and capacity for screening is a problem of interest at airports, seaports, and other ports of entry. Adversaries can observe screening procedures and arrive at a time when there will be gaps in screening due to limited resource capacities. To capture this game between ports and adversaries, this problem has been previously represented as a Stackelberg game, referred to as a Threat Screening Game (TSG). Given the significant complexity associated with solving TSGs and uncertainty in arrivals of customers, existing work has assumed that screenees arrive and are allocated security resources at the beginning of the time-window. In practice, screenees such as airport passengers arrive in bursts correlated with flight time and are not bound by fixed time-windows. To address this, we propose an online threat screening model in which the screening strategy is determined adaptively as a passenger arrives while satisfying a hard bound on acceptable risk of not screening a threat. To solve the online problem, we first reformulate it as a Markov Decision Process (MDP) in which the hard bound on risk translates to a constraint on the action space and then solve the resultant MDP using Deep Reinforcement Learning (DRL). To this end, we provide a novel way to efficiently enforce linear inequality constraints on the action output in DRL. We show that our solution allows us to significantly reduce screenee wait time without compromising on the risk.

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