Improved results on admissibility analysis for singular systems with periodically time-varying delay

This paper is concerned with the admissibility analysis for the singular system with a periodically time-varying delay. By dividing the periodic delay interval into monotonically decreasing and increasing intervals, a novel Lyapunov–Krasovskii functional (LKF) is developed in virtue of the looped functional philosophy, relaxing the positive definition of the LKF and making full use of the system state and the time-varying delay function information. Then, a new admissibility condition is derived by combining the newly constructed LKF and the second-order canonical Bessel–Legendre integral inequality. Finally, two numerical examples are given to demonstrate the superiority of the developed method.     

Asynchronous boundary stabilization for T-S fuzzy Markov jump delay reaction-diffusion neural networks

This paper deals with the exponential boundary stabilization for a class of Markov jump reaction-diffusion neural networks (MJRDNNs) with mixed time-varying delays, which is described by T-S fuzzy model. It is assumed that observed modes in boundary controller are not synchronized with the system modes. Based on a hidden Markov model (HMM), a novel asynchronous boundary control law is developed by using observed modes. Compared with the existing control strategies for distributed parameter systems, the asynchronous boundary control scheme can not only save the cost of the controller installation, but also bring less conservativeness. A delay-dependent sufficient condition to guarantee the exponentially mean square stability is established for T-S fuzzy MJRDNNs with mixed time-varying delays by constructing a Lyapunov functional and utilizing the vector-value Wirtinger-type inequality. Meanwhile, in order to get the designing scheme of the boundary controller, an equivalent LMI-based sufficient criterion is established. In the end, the effectiveness of the proposed results is illustrated by simulation examples.     

Trajectory tracking and disturbance rejection control of random linear systems

A more general trajectory tracking and disturbance rejection problem for the random linear system is considered in this paper. The known structural properties of the reference and the disturbance signals motivate us to use the internal model principle, and the motion decomposition for the linear systems prompts us to utilize the superposition principle. Combining the spectral analysis method with the pole assignment algorithm, the stability and tracking property can be obtained. The simulation result demonstrates that all the closed-loop system is exponentially practically stable in the mean square (EpS-2-M), the tracking error can be regulated to an arbitrarily small constant by turning the parameters.     

An event-triggered model predictive control with exponentially stable offset free for PWA systems with model-plant mismatch

A new event-triggered model predictive control (MPC) method is proposed for PWA systems with model-plant mismatch, such that, for a given asymptotically constant reference signal, exponential stability and unbiased tracking can be achieved. Firstly, for the PWA system, an observer is designed to estimate state and the model-plant mismatch. To guarantee the exponential stability, an event is introduced, and an event-triggered model predictive controller is designed as well. By introducing state error and estimate error, an augmented model is constructed. Then, using the model-dependent average dwell time (MDADT) method and Lyapunov stability theory, exponentially stable condition of the closed-loop system is derived, which is formulated by linear matrix inequalities (LMIs), and zero offset is also guaranteed under some mild assumptions. Moreover, the MDADT of each sub-system is given simultaneously. Finally, simulations have been taken to verify the effectiveness of the proposed method.     

Simultaneous exponential stabilization for stochastic port-controlled Hamiltonian systems with actuator saturations,fading channels and delays

This paper is concerned with the simultaneous exponential stabilization problem for a set of stochastic port-controlled Hamiltonian (PCH) systems. Due to the limited bandwidth of the channels, the phenomena of fading channels and transmission delays which are described by a time-varying stochastic model always occur in the communication channels from the controller to the actuator. Meanwhile, actuator saturation constraint is taken into account. On the basis of dissipative Hamiltonian structural and saturating actuator properties, those stochastic PCH systems are combined to generate an augmented system. By utilizing the stochastic analysis theory, sufficient criterions are given for the simultaneous stabilization controller design ensuring that the closed-loop system is simultaneously exponentially mean-square stable (SEMSS). For the case that there exist external disturbances in the systems, some results on stability analysis and controller design are given. The developed controller design scheme is proved by a three-helicopter model simulation example.     

企业加速网络的兴起及治理结构

运用文献分析和逻辑推理方法分析技术创新服务体系,指出其专业分工细化、功能模块化和网络集成化特征。企业加速网络集成模块化的专业服务,能够实现规模经济、范围经济和速度经济,是助推新兴产业发展和提升创新绩效的重要创新服务组织。采用理想类型方法构想出集中式、分布式和混合式等三种企业加速网络治理结构。建议针对重要行业和条件较好的区域构建企业加速网络,借此提升创新服务绩效。     

基于社会网络分析的企业隐藏犯罪探测的模糊评价模型

本文给出了一种基于社会网络分析(SNA)方法进行企业中犯罪活动的探测方法,通过建立反映企业内部人员社会活动特征的指标,综合这些指标利用模糊评价模型对所有人员的嫌疑程度进行综合评价,从而将参与犯罪团伙的可能性定量地刻画出来,并利用2012美国大学生数学建模竞赛(ICM)中数据对模型进行了运用及验证。     

平衡损失函数下广义Pareto分布参数的Bayes估计的可容许性

对于一个参数进行估计来讲,不仅要考虑估计的精度,还要考虑所得到的估计对于模型拟合的优良程度。通常的损失函数无能为力,而平衡损失函数可以考虑到这两点,本文将在平衡误差损失函数下研究广义Pareto分布参数的Bayes估计问题。在平衡损失函数下导出了参数的Bayes估计并讨论了一类线性形式估计的可容许性和不可容许性。     

Adaptive sliding mode control for persistent dwell-time switched nonlinear systems with matched/mismatched uncertainties and its application

In this paper, the adaptive sliding mode control issue for switched nonlinear systems with matched and mismatched uncertainties is addressed, where the persistent dwell-time switching rule is introduced to describe the switching of parameters. Besides, considering the case that the upper bound of the matched uncertainty is unknown, the purpose of this paper is to utilize an adaptive control method to estimate its upper bound parameters. To begin with, a linear sliding surface is constructed, and then the reduced-order sliding mode dynamics can be obtained through a reduced-order method. Next, sufficient conditions can be derived based on the Lyapunov stability and the persistent dwell-time switching analysis techniques ensuring that the reduced-order sliding mode dynamics is globally uniformly exponentially stable. Moreover, a switched adaptive sliding mode control law is designed, which can not only ensure the reachability of the sliding surface but also estimate the upper bound parameters of the matched uncertainty. Finally, a numerical example and a circuit model are introduced to verify the effectiveness of the proposed method.     

An event-triggered approach to distributed H∞ state estimation for state-saturated systems with randomly occurring mixed delays

In this paper, the event-triggered distributed H_∞ state estimation problem is investigated for a class of state-saturated systems with randomly occurring mixed delays over sensor networks. The mixed delays, which comprise both discrete and distributed delays, are allowed to occur in a random manner governed by two mutually independent Bernoulli distributed random variables. In order to alleviate the communication burden, an event-triggered mechanism is utilized for each sensor node to decide whether or not its current information should be broadcasted to its neighbors. The aim of this paper is to design event-triggered state estimators such that the error dynamics of state estimation is exponentially mean-square stable with a prescribed H_∞ performance index. By resorting to intensive stochastic analysis, sufficient conditions are first derived to guarantee the existence of the desired estimators, and the parameters of the desired distributed estimators are then obtained in light of the feasibility of a certain set of matrix inequalities. A numerical example is employed to illustrate the usefulness of the proposed distributed estimation algorithm.     

Observer-based boundary control of semi-linear parabolic PDEs with non-collocated distributed event-triggered observation

This paper deals with the problem of boundary control for a class of semi-linear parabolic partial differential equations (PDEs) with non-collocated distributed event-triggered observation. A semi-linear Luenberger PDE observer with an output error based event-triggering condition is constructed by using the event-triggered observation to exponentially track the PDE state. By the estimated state, a feedback controller is proposed. It has been shown by the Lyapunov technique, and a variant of Poincaré–Wirtinger inequality that the resulting closed-loop coupled PDEs is exponentially stable if a sufficient condition presented in terms of standard linear matrix inequality (LMI) is satisfied. Moreover, a rigorous proof is provided for existence of a minimal dwell-time between two triggering times. Finally, numerical simulation results are given to show the effectiveness of the proposed design method.     

Event-based sliding mode control for fuzzy singular systems with semi-Markovian switching parameters

This paper addresses the observer-based dynamic event-triggered (DET) sliding mode control (SMC) problem for fuzzy singular semi-Markovian jump systems (FSS-MJSs) subject to generalized dissipative performance, in which a novel double-quantized structure is reasonably merged into a unified model. The main aim of this paper is to develop a mode-dependent adaptive sliding mode control (ASMC) law through the DET rule, which not only makes the closed-loop systems mean-square admissibility and generalized dissipative, but also the finite-time reachability around the predefined sliding mode surface (SMS) can be achieved. Firstly, in order to improve the data transmission efficiency and save network bandwidth resources, DET and doubled-quantized-based control protocol are introduced, in which the event-based threshold function is dynamically regulated and the data of input and output are both quantized; Secondly, due to the sensor information constraints, system state information is not always obtained in practice, hence, a suitable observer design can make up for this defect. Meantime, in terms of elegant linearization technique and implicit function theorem, the uniqueness of the solution for FSS-MJSs is also established; Additionally, by making use of the Lyapunov functional and linear matrix inequality (LMI) technique, both the desired SMC gains, observer gains and triggering parameter matrices are co-designed, more than that the derivative singular matrix is also integrated into the whole design process such that the derived conditions are much more easily to be checked; Finally, a numerical example and a practical application example are co-given to verify the effectiveness of our design mentality.     

Social media opinion summarization using emotion cognition and convolutional neural networks

Quickly and accurately summarizing representative opinions is a key step for assessing microblog sentiments. The Ortony-Clore-Collins (OCC) model of emotion can offer a rule-based emotion export mechanism. In this paper, we propose an OCC model and a Convolutional Neural Network (CNN) based opinion summarization method for Chinese microblogging systems. We test the proposed method using real world microblog data. We then compare the accuracy of manual sentiment annotation to the accuracy using our OCC-based sentiment classification rule library. Experimental results from analyzing three real-world microblog datasets demonstrate the efficacy of our proposed method. Our study highlights the potential of combining emotion cognition with deep learning in sentiment analysis of social media data.     

基于混合溢出的技术创新联盟博弈分析

在AJ模型的基础上,考虑存在混合溢出的情况下双寡头企业生产同质产品时三阶段博弈过程中的四种情形。将技术联盟中共享信息、R&D合作、共享技术成果等内容综合考虑,采用逆向归纳法求均衡解,比较混合溢出大小对产量、利润以及研发成果的影响大小。结论显示混合溢出中的内生溢出存在某个临界值(0.5),在临界值的上下界对于产量、利润、研发成果的影响大小不同。最后,针对处于不同阶段的企业提出不同的联盟建议,并进一步提出了研究的不足以及未来的研究方向。     

基于博弈分析的公众参与环境保护的研究

为了解决我国环境保护现存的一些问题,并为我国的环保事业提供可以实施的措施。在分析我国环保问题的基础上,运用博弈分析的基础理论和相关博弈分析模型。利用混合策略矩阵的分析方法,对该博弈模型进行分析,成功的得到环境保护中必须有公众参与的结论,并且针对性的提出了促进公众参与环境保护工作的措施。     

Deep learning-based sentiment classification of evaluative text based on Multi-feature fusion

Sentiment analysis concerns the study of opinions expressed in a text. Due to the huge amount of reviews, sentiment analysis plays a basic role to extract significant information and overall sentiment orientation of reviews. In this paper, we present a deep-learning-based method to classify a user's opinion expressed in reviews (called RNSA).To the best of our knowledge, a deep learning-based method in which a unified feature set which is representative of word embedding, sentiment knowledge, sentiment shifter rules, statistical and linguistic knowledge, has not been thoroughly studied for a sentiment analysis. The RNSA employs the Recurrent Neural Network (RNN) which is composed by Long Short-Term Memory (LSTM) to take advantage of sequential processing and overcome several flaws in traditional methods, where order and information about the word are vanished. Furthermore, it uses sentiment knowledge, sentiment shifter rules and multiple strategies to overcome the following drawbacks: words with similar semantic context but opposite sentiment polarity; contextual polarity; sentence types; word coverage limit of an individual lexicon; word sense variations. To verify the effectiveness of our work, we conduct sentence-level sentiment classification on large-scale review datasets. We obtained encouraging result. Experimental results show that (1) feature vectors in terms of (a) statistical, linguistic and sentiment knowledge, (b) sentiment shifter rules and (c) word-embedding can improve the classification accuracy of sentence-level sentiment analysis; (2) our method that learns from this unified feature set can obtain significant performance than one that learns from a feature subset; (3) our neural model yields superior performance improvements in comparison with other well-known approaches in the literature.     

On the exponential stabilization of a flexible structure with dynamic delayed boundary conditions via one boundary control only

This study deals with the stability analysis of a flexible structure with one and only one boundary control. The system is composed of three parts: a cart (motorized platform), a flexible cable, and a load mass attached to the lower part of the cable. This situation leads to a hybrid system as a mathematical model for the cable dynamics: one partial differential equation coupled to two ordinary differential equations. Despite the presence of a time-delay in the top-end of the cable, we are able to prove that the hybrid system is well-posed in the sense of semigroups theory and more importantly, only one boundary control can guarantee the exponentially decay of the energy of the system under reasonable conditions on the parameters of the system. This outcome considerably improves the result recently established in [17], where two more controls are required: one interior (Kelvin–Voigt) damping which acts over the entire cable and another boundary control which is exerted on the lower-end of the cable. Furthermore, we provide an estimate of the exponential decay of the system by means an appropriate Lyapunov functional. Lastly, numerical examples are presented in order to ascertain and highlight our theoretical outcomes.