Active set truncated-Newton algorithm for simultaneous optimization of distillation column

An active set truncated-Newton algorithm (ASTNA) is proposed to solve the large-scale bound constrained sub-problems. The global convergence of the algorithm is obtained and two groups of numerical experiments are made for the various large-scale problems of varying size. The comparison results between ASTNA and the subspace limited memory quasi-Newton algorithm and between the modified augmented Lagrange multiplier methods combined with ASTNA and the modified barrier function method show the stability and effectiveness of ASTNA for simultaneous optimization of distillation column.     

Design of pole-assignment controller for systems with algebraic-equation constraint

In this paper we have studied the control of linear systems with algebraic-equation constraint. A pole-assignment controller with two-layer structure for such constrained systems has been presented. The objective of the controller is to assign the desired poles to constrained system and simultaneously force the corresponding closed-loop system to satisfy the constraint. The conditions for the existence of pole-assignment controller are discussed in different cases. The implementation algorithm of pole-assignment controller is also presented. An example is given to illustrate the validity of the method in this paper.     

Identification environment and robust forecasting for nonlinear time series

In this paper, the methods of time series for nonlinearity are briefly surveyed, with particular attention paid to a new test design based on a neural network specification. The proposed integrated expert system contains two main components: an identification environment and a robust forecasting design. The identification environment can be viewed as a integrated dynamic design in which cognitive capabilities arise as a direct consequence of their self-organizational properties. The integrated framework used for discussing the similarities and differences in the nonlinear time series behavior is presented. Moreover, its performance in prediction proves to be superior than the former work. For the investigation of robust forecasting, we perform a simulation study to demonstrate the applicability and the forecasting performance.     

Finite-time coordination in multiagent systems using sliding mode control approach

Finite-time stability in dynamical systems theory involves systems whose trajectories converge to an equilibrium state in finite time. In this paper, we use the notion of finite-time stability to apply it to the problem of coordinated motion in multiagent systems. Specifically, we consider a group of agents described by fully actuated Euler–Lagrange dynamics along with a leader agent with an objective to reach and maintain a desired formation characterized by steady-state distances between the neighboring agents in finite time. We use graph theoretic notions to characterize communication topology in the network determined by the information flow directions and captured by the graph Laplacian matrix. Furthermore, using sliding mode control approach, we design decentralized control inputs for individual agents that use only data from the neighboring agents which directly communicate their state information to the current agent in order to drive the current agent to the desired steady state. Sliding mode control is known to drive the system states to the sliding surface in finite time. The key feature of our approach is in the design of non-smooth sliding surfaces such that, while on the sliding surface, the error states converge to the origin in finite time, thus ensuring finite-time coordination among the agents in the network. In addition, we discuss the case of switching communication topologies in multiagent systems. Finally, we show the efficacy of our theoretical results using an example of a multiagent system involving planar double integrator agents.     

Fisher discrimination based low rank matrix recovery for face recognition

In this paper, we consider the issue of computing low rank (LR) recovery of matrices with sparse errors. Based on the success of low rank matrix recovery in statistical learning, computer vision and signal processing, a novel low rank matrix recovery algorithm with Fisher discrimination regularization (FDLR) is proposed. Standard low rank matrix recovery algorithm decomposes the original matrix into a set of representative basis with a corresponding sparse error for modeling the raw data. Motivated by the Fisher criterion, the proposed FDLR executes low rank matrix recovery in a supervised manner, i.e., taking the with-class scatter and between-class scatter into account when the whole label information are available. The paper shows that the formulated model can be solved by the augmented Lagrange multipliers and provides additional discriminating power over the standard low rank recovery models. The representative bases learned by the proposed method are encouraged to be closer within the same class, and as far as possible between different classes. Meanwhile, the sparse error recovered by FDLR is not discarded as usual, but treated as a feedback in the following classification tasks. Numerical simulations demonstrate that the proposed algorithm achieves the state of the art results.     

基于重加权快速交替方向法的频谱感知

在认知无线电网络中,由于深衰落和低信噪比的影响,单个认知用户的宽带频谱检测性能较差,且算法复杂度较高。针对该问题,提出一种基于重加权快速交替方向法的频谱感知算法。利用目标函数的凸性,通过求导简化辅助变量的更新过程。对目标函数进行线性化处理,增加一个二次项,使待估变量更新时部分项线性化的增广拉格朗日函数成为严格凸函数,并使用迭代软阈值算法进行求解。在目标项中增加大权值抑制信号中的非零元素,获得接近于最小?0范数的解。实验结果表明,该算法能有效提高低信噪比环境下的检测概率和检测速度。     

极C-invex条件下多目标非线性规划问题的对称对偶性

提出了一个多目标非线性规划的对称对偶性问题,并利用弱有效性和真有效性的概念,证明了在极C-invex条件下与规划问题相关的弱对偶定理、强对偶定理、逆对偶定理和自对偶定理.     

与特殊性质&对偶的空间及相关结论

X是拓扑空间,令&={A:A是X的具有性质&的子集},如果对于X的任意邻域指派φ,都存在A∈&,使得X=U{φ(χ):χ∈A},则称X是与性质&对偶的空间.对于给定的特殊性质&,主要讨论了与性质&对偶的空间的一些基本性质,并给出了X是与性质&对偶空间的充分必要条件.这些结论可应用于多种空间类,作为其中的一推论,得到每个正则弱(8)-加细(离散对偶)-散布空间是离散对偶空间.另外,还讨论了aD-空间的相关结论.

Abstract：

Let X be a space,and &={A:A is a subset of X,and has property &}.A space X is dual the property & if for any neighborhood assignment φ for X,there is a subset A(X,A∈& such that X=U{φ (χ):χ∈ A}.In this note,we mainly discuss properties of spaces which are dually special &,and also give a necessary and sufficient condition for spaces which are dually special & These conclusions can be heId for many spaces.As a corollary,we have that if X is a regular weak (8)-refinable(dually discrete)-scattered space,then X is dually discrete.We also get some conclusions conserning aD-spaces.     

德国“双元制”职业教育对我国女性职教改革的启示——以广东女子职业技术学院为例

德国"双元制"职业教育以其行动导向的实践与理论,企业与学校紧密结合的学习,即学习与工作结合,在工作中学习的职业教育模式,突显其价值与效益,在国际职业教育比较中颇具强势。文章从高职教育的发展趋势,对"双元制"职业教育的特点和结构改革进行分析和探讨,提出了发展我国职教尤其是女性职业教育改革的几点建议。