Simultaneous state estimation and parameter identification in linear fractional order systems using coloured measurement noise

In the present paper, the identification and estimation problem of a single-input–single-output (SISO) fractional order state-space system will be addressed. A SISO state-space model is considered in which parameters and also state variables should be estimated. The canonical fractional order state-space system will be transformed into a regression equation by using a linear transformation and a shift operator that are appropriate for identification. The identification method provided in this paper is based on a recursive identification algorithm that has the capability of identifying the parameters of fractional order state-space system recursively. Another subject that will be addressed in this paper is a novel fractional order Kalman filter suitable for the systems with coloured measurement noise. The promising performance of the proposed methods is verified using two stable fractional order systems.     

Bayesian zero-order TSK fuzzy system modeling

Different from the existing TSK fuzzy system modeling methods, a novel zero-order TSK fuzzy modeling method called Bayesian zero-order TSK fuzzy system (B-ZTSK-FS) is proposed from the perspective of Bayesian inference in this paper. The proposed method B-ZTSK-FS constructs zero-order TSK fuzzy system by using the maximum a posteriori (MAP) framework to maximize the corresponding posteriori probability. First, a joint likelihood model about zero-order TSK fuzzy system is defined to derive a new objective function which can assure that both antecedents and consequents of fuzzy rules rather than only their antecedents of the most existing TSK fuzzy systems become interpretable. The defined likelihood model is composed of three aspects: clustering on the training set for antecedents of fuzzy rules, the least squares (LS) error for consequent parameters of fuzzy rules, and a Dirichlet prior distribution for fuzzy cluster memberships which is considered to not only automatically match the “sum-to-one” constraints on fuzzy cluster memberships, but also make the proposed method B-ZTSK-FS scalable for large-scale datasets by appropriately setting the Dirichlet index. This likelihood model indeed indicates that antecedent and consequent parameters of fuzzy rules can be linguistically interpreted and simultaneously optimized by the proposed method B-ZTSK-FS which is based on the MAP framework with the iterative sampling algorithm, which in fact implies that fuzziness and probability can co-jointly work for TSK fuzzy system modeling in a collaborative rather than repulsive way. Finally, experimental results on 28 synthetic and real-world datasets are reported to demonstrate the effectiveness of the proposed method B-ZTSK-FS in the sense of approximation accuracy, interpretability and scalability.     

基于Cosserat连续体理论的疲劳寿命概率可靠性分析

基于Cosserat连续体理论建立考虑应变梯度效应的粉末高温合金弹塑性模型.该模型考虑微尺度长度效应,并能消除缺陷局部化问题求解时的网格依赖性.基于参变量变分原理提出一种高效的求解方法,该方法将原非线性问题转化为互补问题求解,可大大提高求解效率和收敛性.针对一种假想涡轮盘模型,将Cosserat尺度参数和Basquin斜率作为随机变量,分别采用蒙特卡洛法与二阶矩法进行疲劳寿命概率可靠度计算.结果表明二阶矩法在保证精度的情况下具有较高的效率.     

以信息安全控制为原理的安全网格技术分析

主要对超大规模集成电路（GSI）及网格系统的安全性进行研究.基于网格系统的动态特性和可扩展性,提出信息安全控制的思想,着重对信息安全控制原理的内容及基本理论进行阐述.通过研究,实现了具有反馈功能的安全网络模型的构建,网络模型包含安全控制策略库、安全网格控制及安全网格认证等模块组成,从而极大地提高了网格系统的动态性能和可扩展性.     

基于一次密钥的数据加密算法优化

介绍了关于DES算法的一些研究情况, 分析了DES了算法的加密原理以及实现过程, 通过内容分析, 简单介绍了DES算法的几种改进方案, 通过分析它的加密过程以及几种改进方案, 进而提出了自己的一个改进思想即一次密钥的加密方案, 通过这个方案为DES算法的优化作一个参考.     

A second-order maximum principle for discrete-time bilinear control systems with applications to discrete-time linear switched systems

A powerful approach for analyzing the stability of continuous-time switched systems is based on using optimal control theory to characterize the “most unstable” switching law. This reduces the problem of determining stability under arbitrary switching to analyzing stability for the specific “most unstable” switching law. For discrete-time switched systems, the variational approach received considerably less attention. This approach is based on using a first-order necessary optimality condition in the form of a maximum principle (MP), and typically this is not enough to completely characterize the “most unstable” switching law. In this paper, we provide a simple and self-contained derivation of a second-order necessary optimality condition for discrete-time bilinear control systems. This provides new information that cannot be derived using the first-order MP. We demonstrate several applications of this second-order MP to the stability analysis of discrete-time linear switched systems.     

An internal model principle is necessary and sufficient for linear output synchronization

Output synchronization of a network of heterogeneous linear state–space models under time-varying and directed interconnection structures is investigated. It is shown that, assuming stabilizability and detectability of the individual systems and imposing very mild connectedness assumptions on the interconnection structure, an internal model requirement is necessary and sufficient for synchronizability of the network to polynomially bounded trajectories. The resulting dynamic feedback couplings can be interpreted as a generalization of existing methods for identical linear systems.     

Control of discrete-time HMM partially observed under fractional Gaussian noises

A discrete-time control problem of a finite-state hidden Markov chain partially observed in a fractional Gaussian process is discussed using filtering. The control problem is then recast as a separated problem with information variables given by the unnormalized conditional probabilities of the whole path of the hidden Markov chain. A dynamic programming result and a minimum principle are obtained.