Design of an adaptive fuzzy sliding mode control for uncertain discrete-time nonlinear systems based on noisy measurements

This paper presents the design of an adaptive fuzzy sliding mode control (AFSMC) for uncertain discrete-time nonlinear dynamic systems. The dynamic systems are described by a discrete-time state equation with nonlinear uncertainties, and the uncertainties include the modelling errors and the external disturbances to be unknown but nonlinear with the bounded properties. The states are measured by the restriction of measurement sensors and the contamination with independent measurement noises. The nonlinear uncertainties are approximated by using the fuzzy IF-THEN rules based on the universal approximation theorem, and the approximation error is compensated by adding an adaptive complementary term to the proposed AFSMC. The fuzzy inference approach based on the extended single input rule modules is proposed to reduce the number of the fuzzy IF-THEN rules. The estimates for the un-measurable states and the adjustable parameters are obtained by using the weighted least squares estimator and its simplified one. It is proved that under some conditions the estimation errors will remain in the vicinity of zero as time increases, and the states are ultimately bounded subject to the proposed AFSMC. The effectiveness of the proposed method is indicated through the simulation experiment of a simple numerical system.     

分数阶系统变阶次状态空间建模及稳定理论

结合分数阶微积分算子的叠加原理,提出了变阶次状态空间建模方法.将分数阶系统推广到状态空间领域,实现了最小阶状态空间转换,并可根据实际需要通过增加状态变量来提取某一阶次的输出信号.对于各阶次均小于1的变阶次状态空间实现的分数阶系统,提出了变阶次分数阶系统的稳定性判定定理.最后通过实例仿真验证了所提出方法的有效性.     

基于Bayes定理和mGA的结构损伤识别方法研究

提出了一种基于幽ycs定理和沐从(微种群遗传算法)的方法,用于识别结构中损伤的位置和程度等参数并分析其不确定性。该方法在获得测量信息后,结合损伤模型,根据Bayes定理更新获得损伤模型参数的后验概率密度函数,以实现在不确定性情况下对损伤的识别。为最大化损伤模型参数的后验概率密度函数,采用mGA搜索获得描述损伤的全局最优参数。将该方法应用于板结构损伤识别,并进行了数值仿真研究以验证所提方法的有效性。     

免疫分类研究进展

过去的十多年中,人工免疫系统得到了长足的发展。由于其具备强大的信息处理能力,被广泛应用于模式识别领域,尤其是分类问题。介绍了免疫系统的识别原理、人工免疫原理,包括克隆选择原理、负向选择原理和免疫网络,综述了近年来出现的主要的免疫分类算法,对免疫分类算法性能进行了相互比较,比较了免疫分类算法与其他著名分类算法的性能,结果显示免疫分类算法具有良好的分类性能。     

Ordered Semantic Hyper-Linking

The ordered semantic hyper-linking strategy is complete for first-order logic and accepts a user-specified natural semantics that guides the search for a proof. Any semantics in which the meanings of the function and predicate symbols are computable on ground terms may be used. This instance-based strategy is efficient on near-propositional problems, is goal sensitive, and has an extension to equality and term rewriting. However, it sometimes has difficulty generating large terms. We compare this strategy with some others that use semantic information, and present a proof of soundness and completeness. We also give some theoretical results about the search efficiency of the strategy. Some examples illustrate the performance of the strategy.     

Performance analysis of stochastic gradient algorithms under weak conditions

By using the stochastic martingale theory, convergence properties of stochastic gradient （SG） identification algorithms are studied under weak conditions. The analysis indicates that the parameter estimates by the SG algorithms consistently converge to the true parameters, as long as the information vector is persistently exciting （i.e., the data product moment matrix has a bounded condition number） and that the process noises are zero mean and uncorrelated. These results remove the strict assumptions, made in existing references, that the noise variances and high-order moments exist, and the processes are stationary and ergodic and the strong persis- tent excitation condition holds. This contribution greatly relaxes the convergence conditions of stochastic gradient algorithms. The simulation results with bounded and unbounded noise variances confirm the convergence conclusions proposed.     

Building ensemble classifiers using belief functions and OWA operators

A pervasive task in many forms of human activity is classification. Recent interest in the classification process has focused on ensemble classifier systems. These types of systems are based on a paradigm of combining the outputs of a number of individual classifiers. In this paper we propose a new approach for obtaining the final output of ensemble classifiers. The method presented here uses the Dempster–Shafer concept of belief functions to represent the confidence in the outputs of the individual classifiers. The combing of the outputs of the individual classifiers is based on an aggregation process which can be seen as a fusion of the Dempster rule of combination with a generalized form of OWA operator. The use of the OWA operator provides an added degree of flexibility in expressing the way the aggregation of the individual classifiers is performed.     

Computational origami environment on the web

We present a computing environment for origami on the web. The environment consists of the computational origami engine Eos for origami construction, visualization, and geometrical reasoning, WebEos for providing web interface to the functionalities of Eos, and web service system Scorum for symbolic computing web services. WebEos is developed using Web2.0 technologies, and provides a graphical interactive web interface for origami construction and proving. In Scorum, we are preparing web services for a wide range of symbolic computing systems, and are using these services in our origami environment. We explain the functionalities of this environment, and discuss its architectural and technological features.     

Espaces métriques rationnellement présentés et complexité, le cas de l'espace des fonctions réelles uniformément continues sur un intervalle compact

We define the notion of rational presentation of a complete metric space, in order to study metric spaces from the algorithmic complexity point of view. In this setting, we study some representations of the space C[0,1] of uniformly continuous real functions over [0,1] with the usual norm: ||f||_∞ = Sup{|f(x)|; 0x1}. This allows us to have a comparison of global kind between complexity notions attached to these presentations. In particular, we get a generalization of Hoover's results concerning the Weierstrass approximation theorem in polynomial time. We get also a generalization of previous results on analytic functions which are computable in polynomial time.