Unified stabilizing controller synthesis approach for discrete-time intelligent systems with time delays by dynamic output feedback

A novel model, termed the standard neural network model (SNNM), is advanced to describe some delayed (or non-delayed) discrete-time intelligent systems com- posed of neural networks and Takagi and Sugeno (T-S) fuzzy models. The SNNM is composed of a discrete-time linear dynamic system and a bounded static nonlinear operator. Based on the global asymptotic stability analysis of the SNNMs, linear and nonlinear dynamic output feedback controllers are designed for the SNNMs to stabilize the closed-loop systems, respectively. The control design equations are shown to be a set of linear matrix inequalities (LMIs) which can be easily solved by various convex optimization algorithms to determine the control signals. Most neural-network-based (or fuzzy) discrete-time intelligent systems with time delays or without time delays can be transformed into the SNNMs for controller synthesis in a unified way. Three application examples show that the SNNMs not only make controller synthesis of neural-network-based (or fuzzy) discrete-time intelligent systems much easier, but also provide a new approach to the synthesis of the controllers for the other type of nonlinear systems.     

Information properties of the simplest feedback operator and certain conclusions

A study of the response to a random input signal of the simplest operator with constant transfer factor in a direct channel where there is negative feedback present is carried out. It is shown that feedback in the channel exerts a positive influence on the information properties of the output quantity. It is suggested that the results of the study can be recommended for the construction of reliable computations in future computers and other information systems.     

两个不同Sprott混沌系统的控制与同步研究

首先采用线性反馈方法来控制混沌的SprottC系统和混沌的SprottF系统,使这两个混沌系统的轨道被控制到他们各自的每一个平衡点.然后使用主动控制方法来同步这两个不同的混沌系统,使混沌的SprottC系统和SprottF系统的轨道重合.并用数值模拟证实了理论的正确性.     

仿射非线性奇异系统的受控分布

研究非线性奇异系统的受控不变分布问题.讨论了非线性奇异系统的受控不变分布算法与该系统经过状态反馈转化为正常非线性系统的受控不变分布算法的关系.得到了在一定条件下,正则非线性奇异系统的受控不变分布算法与该系统经过状态反馈转化为正常非线性系统的受控不变分布算法的一致性.并给出一个例子说明本文的结果.     

Feedback Control of Spin Systems

The feedback stabilization problem for ensembles of coupled spin 1/2 systems is discussed from a control theoretic perspective. The noninvasive nature of the bulk measurement allows in principle for a fully unitary and deterministic closed loop. The Lyapunov-based feedback design presented does not require spins that are selectively addressable. With this method, it is possible to obtain control inputs also for difficult tasks, like suppressing undesired couplings in identical spin systems.      

The analysis of embodied communicative feedback in multimodal corpora: a prerequisite for behavior simulation

Communicative feedback refers to unobtrusive (usually short) vocal or bodily expressions whereby a recipient of information can inform a contributor of information about whether he/she is able and willing to communicate, perceive the information, and understand the information. This paper provides a theory for embodied communicative feedback, describing the different dimensions and features involved. It also provides a corpus analysis part, describing a first data coding and analysis method geared to find the features postulated by the theory. The corpus analysis part describes different methods and statistical procedures and discusses their applicability and the possible insights gained with these methods.     

基于嵌入式RTOS的闭环反馈调度算法的研究

分析了常见调度算法的特点，提出一种基于嵌入式实时操作系统的闭环反馈控制调度策略。针对任务的特点．从任务的周期与非周期性、I／O消耗和处理器消耗型两个方面对任务进行相应的反馈控制，使调度器具有一定程度上的自适应功能。并对实时操作系统μC／OS-Ⅱ的内核调度算法进行改进，同时与EDF算法进行对比测试，可以看出改进后的调度算法在系统负载较重或系统过载的情况下表现良好。     

磁悬浮列车的双环控制

为了增强控制系统对磁悬浮列车系统参数变化的适应性,将磁悬浮系统分为电流环和位置环进行控制.对于电流环系统,采用模型参考自适应控制方法进行控制,使得在电磁线圈参数变化的情况下,电流环的性能能够保持稳定,这样就将三阶非线性磁悬浮系统降阶为二阶系统;对于二阶系统,首先采用反馈线性化控制方法将系统线性化,然后采用PD控制算法进行控制,从而确保系统在不同工作点处的性能一致.仿真实验结果证明,该方法使磁悬浮列车的适应能力得到了明显的提高.     

Chinese character recognition :history ,status and prospects

Chinese character recognition (CCR) is an important branch of pattern recognition. It was considered as an extremely difficult problem due to the very large number of categories, complicated structures, similarity between characters, and the variability of fonts or writing styles. Because of its unique technical challenges and great social needs, the last four decades witnessed the intensive research in this field and a rapid increase of successful applications. However, higher recognition performance is continuously needed to improve the existing applications and to exploit new applications. This paper first provides an overview of Chinese character recognition and the properties of Chinese characters. Some important methods and successful results in the history of Chinese character recognition are then summarized. As for classification methods, this article pays special attention to the syntactic-semantic approach for online Chinese character recognition, as well as the metasynthesis approach for discipline crossing. Finally, the remaining problems and the possible solutions are discussed.