Helping ants for adaptive network routing

Appropriate routing in data transfer is a challenging problem that can lead to improved performance of networks in terms of lower delay in delivery of packets and higher throughput. Considering the highly distributed nature of networks, several multi-agent based algorithms, and in particular ant colony based algorithms, have been suggested in recent years. However, considering the need for quick optimization and adaptation to network changes, improving the relative slow convergence of these algorithms remains an elusive challenge. Our goal here is to reduce the time needed for convergence and to accelerate the routing algorithm's response to network failures and/or changes by imitating pheromone propagation in natural ant colonies. More specifically, information exchange among neighboring nodes is facilitated by proposing a new type of ant (helping ants) to the AntNet algorithm. The resulting algorithm, the “modified AntNet,” is then simulated via NS2 on NSF network topology. The network performance is evaluated under various node-failure and node-added conditions. Statistical analysis of results confirms that the new method can significantly reduce the average packet delivery time and rate of convergence to the optimal route when compared with standard AntNet.     

Optimal tracking performance and design of networked control systems with packet dropouts

The optimal tracking problem for single-input–single-output (SISO) networked control system over a communication channel with packet dropouts is studied in this paper. The tracking performance is measured by the energy of the error signal between the output of the plant and the reference signal. It is shown that the optimal tracking performance is constrained by nonminimum phase zeros, unstable poles, the characteristics of the reference signal and packet dropout probability, and the optimal controller is obtained. It is also shown that when the communication constraint does not exist, the optimal tracking performance reduces to the existing normal tracking performance of the control system without a communication channel. The result shows how the packet dropouts probability of a communication channel may fundamentally constrain a control system's tracking ability. Some typical examples and simulations are given to illustrate the theoretical results.     

融合传播视域下大学生理想信念教育的路径探析

融合传播将传统媒体与新媒体资源进行整合,在大学生的理想信念教育上,能够实现“漫灌”变“滴灌”、“单程”变“全程”、“单一”变“融合”,增强了育人的效果,并探讨通过传播主体、传播内容、传播渠道的融合,来增强对大学生理想信念的价值引领和情感认同。     

Disturbance and state observer-based adaptive finite-time control for quantized nonlinear systems with unknown control directions

This paper concentrates on proposing a novel finite-time tracking control algorithm for a kind of nonlinear systems with input quantization and unknown control directions. The nonlinear functions in the system are approximated by the means of strong approximation capability of the fuzzy logic systems. Firstly, the nonlinear system with unknown control directions is transformed into an equivalent system with known control gains by coordinate transformation. Secondly, the unknown system states are estimated by a designed fuzzy state observer, and the disturbance observer is constructed to track the external disturbances. The command filtering method is proposed to approach the problem of “explosion of complexity” existed in the conventional backstepping design process. In this system, the difficulties caused by unknown control directions are solved via the Nussbaum gain approach. Finally, based on the fuzzy state observer, the controller of the original system is obtained via using the transformed system by the backstepping method. The boundedness of all signals and the convergence of tracking and observer errors at the origin are ensured for the closed-loop system, and demonstrated by the simulation result in this paper.     

Distributed adaptive event-triggered protocol for tracking control of leader-following multi-agent systems

A novel adaptive event-triggered control protocol is developed to investigate the tracking control problem of multi-agent systems with general linear dynamics. By introducing the event-triggered control strategy, each agent can decide when to transfer its state to its neighbors at its own triggering instants, which can greatly reduce communication burden of agents. It is shown that the “Zeno phenomenon” does not occur by verifying that there exists a positive lower bound on the inter-event time intervals of agents under the proposed adaptive event-triggered control algorithm. Finally, an example is provided to testify the effectiveness of the obtained theoretical results.     

产业集群内企业展示或隐藏创新行为的演化博弈分析

通过建立演化博弈模型分析集群内企业展示或隐藏创新行为演化的过程特征和均衡状态。当产业集群内的知识溢出效应增大时,系统收敛于(展示创新,展示创新)状态的概率增大,即产业集群的企业均坚持展示创新行为,产业集群朝着良性的状态发展。当产业集群内的模仿行为给创新企业带来的损失增大时,系统收敛于(隐藏创新,隐藏创新)状态的概率增大,不利于产业集群内的知识溢出,最终使产业集群走向衰退。针对促进产业集群内企业选择展示创新行为的路径,提出相应的建议。     

New Results in Sridhar Filtering Theory

A nonlinear adaptive filter is introduced and applied to the classical problem of detecting a sinusoidal signal, with unknown frequency, in white noise. The filter is basically a new result in what is known as Sridhar filtering theory. In the derivation of the filter, called the “Pontryagin filter”, the Pontryagin minimum principle and the method of invariant imbedding are used. The stability, bias and convergence properties are also studied and presented.     

Fraction dynamic-surface-based adaptive neural finite-time control for stochastic nonlinear systems subject to unknown control directions,time-varying input delay and state delay

In this paper, an adaptive neural finite-time tracking control is studied for a category of stochastic nonlinearly parameterized systems with multiple unknown control directions, time-varying input delay, and time-varying state delay. To this end, a novel criterion of semi-globally finite-time stability in probability (SGFSP) is proposed, in the sense of Lyapunov, for stochastic nonlinear systems with multiple unknown control directions. Secondly, a novel auxiliary system with finite-time convergence is presented to cope with the time-varying input delay, the appropriate Lyapunov Krasovskii functionals are utilized to compensate for the time-varying state delay, Nussbaum functions are exploited to identify multiple unknown control directions, and the neural networks (NNs) are applied to approximate the unknown functions of nonlinear parameters. Thirdly, the fraction dynamic surface control (FDSC) technique is embedded in the process of designing the controller, which not only the “explosion of complexity” problems are successfully avoided in traditional backstepping methods but also the command filter convergence can be obtained within a finite time to lead greatly improved for the response speed of command filter. Meanwhile, the error compensation mechanism is established to eliminate the errors of the command filter. Then, based on the proposed novel criterion, all closed-loop signals of the considered systems are SGPFS under the designed controller, and the tracking error can drive to a small neighborhood of the origin in a finite time. In the end, three simulation examples are applied to demonstrate the validity of the control method.     

Generalized pseudo-Bayesian estimator for networked control systems over UDP-like channels

This paper focuses on state estimation issues for networked control systems (NCSs) with both control input and observation packet dropouts over user datagram protocol (UDP) communication channels. For such systems, which are usually known as UDP-like systems, the computation cost of the optimal estimator is too high to afford in practice due to exponential growth of complexity. Although quite a few suboptimal estimators could be alternatives for improving the computational efficiency, yet researches on the stability of suboptimal estimators are rarely reported. Based on the generalized pseudo-Bayesian (GPB) algorithm, an efficient suboptimal algorithm is developed for UDP-like systems. More crucially, a sufficient condition is obtained, which guarantees the stability of its mean estimation error covariance. This stability condition explicitly expresses that the rate of observation packet dropout is a critical factor in determining the stability of the proposed GPB estimator, while the rate of control input packet dropout has no influence on it. The results are illustrated by numerical examples.     

Identification of switched FIR systems with random missing outputs: A variational Bayesian approach

Identification of switched finite impulse response (FIR) systems in the presence of random missing outputs is investigated in this paper and the practical problems of unknown number of local models and unknown switching mechanism are handled. From a Bayesian perspective, the probabilistic model for describing the identification problem is constructed and the algorithm to estimate all of the unknown parameters is derived by using the variational Bayesian (VB) approach. In addition, the number of local models can be selected based on the probability of each local component, and the predicted output can be obtained as the output of the local model that takes effect. A simulated example and the mass-spring-damper system are explored to illustrate the efficacy of the developed algorithm.     

Particle filtering based parameter estimation for systems with output-error type model structures

The output-error model structure is often used in practice and its identification is important for analysis of output-error type systems. This paper considers the parameter identification of linear and nonlinear output-error models. A particle filter which approximates the posterior probability density function with a weighted set of discrete random sampling points is utilized to estimate the unmeasurable true process outputs. To improve the convergence rate of the proposed algorithm, the scalar innovations are grouped into an innovation vector, thus more past information can be utilized. The convergence analysis shows that the parameter estimates can converge to their true values. Finally, both linear and nonlinear results are verified by numerical simulation and engineering.     

Modified multi-innovation stochastic gradient algorithm for Wiener–Hammerstein systems with backlash

In this paper, the identification of the Wiener–Hammerstein systems with unknown orders linear subsystems and backlash is investigated by using the modified multi-innovation stochastic gradient identification algorithm. In this scheme, in order to facilitate subsequent parameter identification, the orders of linear subsystems are firstly determined by using the determinant ratio approach. To address the multi-innovation length problem in the conventional multi-innovation least squares algorithm, the innovation updating is decomposed into sub-innovations updating through the usage of multi-step updating technique. In the identification procedure, by reframing two auxiliary models, the unknown internal variables are replaced by using the outputs of the corresponding auxiliary model. Furthermore, the convergence analysis of the proposed algorithm has shown that the parameter estimation error can converge to zero. Simulation examples are provided to validate the efficiency of the proposed algorithm.     

Predictive control of uncertain systems over networks with redundant link

Networked systems using redundant channels to transmit data can effectively reduce the probability of data loss and improve system reliability and control margin. However, the structural complexity and economic cost of the system are also increased. To balance the redundancy and feasibility, the ratio of attraction domain to packet loss rate is defined as a balanced feasibility index. In this paper, single-channel packet loss is considered as Bernoulli distribution and a bounded packet loss network system control model is constructed as the arbitrary bounded packet loss control problem for redundant channel transmission network system. Therefore, the robust conditions of the closed-loop system and the constraints of the input and state are established under the framework of robust predictive control to construct the linear matrix inequality (LMI) optimization problem. Finally, to verify the effectiveness of the design method proposed in this paper, the discrete time-varying linear system and the main steam control system with redundant channels are used as study cases.     

Robust fault detection of linear systems over networks with bounded packet loss

This paper is devoted to the fault detection of linear systems over networks with bounded packet loss. The inputs and the measurements of the monitored system are transmitted to a fault detection node over an unreliable network with bounded packet loss. The packet loss process is assumed to be arbitrary or Markovian in this paper. Due to the bounded packet loss process, the monitored system is modeled as a switched system by re-sampling it at each time instant when the measurements arrive at the fault detection node. A fault detection filter for this switched system is designed in this paper to satisfy some performance constraints. The filter updates only at the time instant when new measurements arrive at the fault detection node and the input data packets' lost are considered as external disturbances. Finally, the numerical example and simulations have demonstrated the usefulness of the proposed method.     

一种α稳定分布下的变步长最小ρ-范数算法

本文提出一种新的基于α稳定分布噪声环境下的自适应滤波算法,这种算法针对变步长自适应滤波算法收敛速度和稳态误差相矛盾的不足,建立了步长μ(n)与误差信号e(n)之间的新的非线性函数关系。该函数能够削弱输入端不相关α稳定分布噪声对步长调整的影响,更好地解决稳态误差与收敛时间之间的矛盾。通过系统辨识仿真结果表明,新的算法α对稳定分布下的尖峰脉冲噪声有较强的韧性,比传统的NLMP算法有更快的参数辨识速度和更小的稳态误差,同时还具有很好地跟踪多时变系统的能力。     

Control of connected vehicles with event-triggered transmission and prescribed energy budget

This paper is concerned with event-triggered cooperative control of a platoon of connected vehicles via vehicular ad hoc networks (VANETs). To reduce communications among vehicles, we introduce a hybrid event-triggered transmission mechanism based on both time elapsed and state error. The effect of time-varying transmission delay and communication energy constraint can be also taken into account in the system modeling and design procedures. The on-board sensors use different power levels to transmit information resulting in different packet loss rates. The vehicular platoon system is proved to be exponentially mean-square stable under the hybrid event-triggering scheme and a constant time headway spacing policy. A framework for co-design of the hybrid event triggering scheme and the output feedback controller is given to guarantee platoon stability and spacing-error convergence along the stream. Numerical simulations are given to demonstrate the effectiveness of proposed method.     

State estimation for networked systems with Markovian communication constraints and multiple packet dropouts

In this paper, the state estimation problem for discrete-time networked systems with communication constraints and random packet dropouts is considered. The communication constraint is that, at each sampling instant, there is at most one of the various transmission nodes in the networked systems is allowed to access a shared communication channel, and then the received data are transmitted to a remote estimator to perform the estimation task. The channel accessing process of those transmission nodes is determined by a finite-state discrete-time Markov chain, and random packet dropouts in remote data transmission are modeled by a Bernoulli distributed white sequence. Using Bayes’ rule and some results developed in this study, two state estimation algorithms are proposed in the sense of minimum mean-square error. The first algorithm is optimal, which can exactly compute the minimum mean-square error estimate of system state. The second algorithm is a suboptimal algorithm obtained under a lot of Gaussian hypotheses. The proposed suboptimal algorithm is recursive and has time-independent complexity. Computer simulations are carried out to illustrate the performance of the proposed algorithms.     

分组零膨胀泊松模型的半参数统计推断

泊松回归模型常常用于计数数据的研究中,然而在实际数据中零值的比例可能远远大于泊松分布中取零值的概率,而且这些零值通常都有其特殊含义.此外计数数据可能是分组数据,即观测到的数据不是确切值而只是已知其落在某一个区间范围之内;或者某些特定的数据,例如工资,要先对它进行人为的分组然后再进行分析.考虑一种零膨胀泊松半参数回归模型来处理上述分组计数数据.该模型中泊松分布的期望与协变量之间采用部分线性连接函数,而零值的概率与协变量之间采用线性连接函数.利用Sieve极大似然估计方法来估计该回归模型中参数和非参数函数,并提出了一种得分检验方法来检验是否存在零膨胀.在一定正则条件下,获得了Sieve极大似然估计的渐近性质,证明了参数部分的估计是强相合,渐近正态及渐近有效的;同时非参数函数的估计达到了最优收敛速度.模拟研究表明,估计和检验方法效果都比较好,最后将此模型和推断方法应用于一组公共卫生领域实际数据研究.     

Citations backward and forward: Insights into the patent examiner's role

Examiners’ instructions and academic studies on patent validity determination focus on identification of “blocking” citations that invalidate claims in applications as non-novel or obvious, generally ignoring the non-blocking majority as irrelevant to validity. Recently available datasets allow us to identify, for the first time, “forward” citations received by applications before grant, as well as “backward” citations in those applications, and distinguish those identified by the examiner as blocking (submitted mainly by examiners), as well as non-blocking examiner and applicant citations. Categorical analysis confirms that blocking citations in an application strongly negatively predict its grant, but positively predict grant of the cited blocking applications. Non-blocking applicant and examiner citations in an application equally strongly predict its grant, but do not predict grant of cited applications. We test whether expected value – measured by applicant forward citations to the application prior to its grant – affects probability of grant, with negative results. These findings expand our understanding of the scope of examiners’ and applicants’ roles as mediators of validity-relevant information in applications.