融合多向学习的混沌麻雀搜索算法

对于原始麻雀搜索算法（SSA）在迭代过程中表现出的种群多样性减小,易陷入局部最优等问题,提出一种融合多向学习的混沌麻雀搜索算法（MSSA）。利用Hénon混沌映射初始化种群,增加麻雀种群的多样性,扩大可行解的搜索范围,为全局寻优奠定基础;采用多向学习策略增加麻雀跟随者探索未知领域的机会,平衡算法的局部开发性能和全局搜索能力;当算法陷入局部最优时,引用遗传算法中的变异策略依据动态的变异概率对当前最优个体进行扰动变异;将MSSA算法应用到无线传感器网络节点覆盖优化问题。数值实验结果与Wilcoxon秩和检验结果均表明MSSA算法在收敛精度与收敛速度等方面具有更明显的优势。     

非零和微分博弈系统的事件触发最优跟踪控制（英文）

近年来,对于具有未知动态的非零和微分博弈系统的跟踪问题,已经得到了讨论,然而这些方法是时间触发的,在传输带宽和计算资源有限的环境下并不适用.针对具有未知动态的连续时间非线性非零和微分博弈系统,本文提出了一种基于积分强化学习的事件触发自适应动态规划方法.该策略受梯度下降法和经验重放技术的启发,利用历史和当前数据更新神经网络权值.该方法提高了神经网络权值的收敛速度,消除了一般文献设计中常用的初始容许控制假设.同时,该算法提出了一种易于在线检查的持续激励条件(通常称为PE),避免了传统的不容易检查的持续激励条件.基于李亚普诺夫理论,证明了跟踪误差和评价神经网络估计误差的一致最终有界性.最后,通过一个数值仿真实例验证了该方法的可行性.     

Heterogeneous clustering via adversarial deep Bayesian generative model

This paper aims to study the deep clustering problem with heterogeneous features and unknown cluster number. To address this issue, a novel deep Bayesian clustering framework is proposed. In particular, a heterogeneous feature metric is first constructed to measure the similarity between different types of features. Then, a feature metric-restricted hierarchical sample generation process is established, in which sample with heterogeneous features is clustered by generating it from a similarity constraint hidden space. When estimating the model parameters and posterior probability, the corresponding variational inference algorithm is derived and implemented. To verify our model capability, we demonstrate our model on the synthetic dataset and show the superiority of the proposed method on some real datasets. Our source code is released on the website: Github.com/yexlwh/Heterogeneousclustering.     

A Study of a Convex Variational Diffusion Approach for Image Segmentation and Feature Extraction

We analyze a variational approach to image segmentation that is based on a strictly convex non-quadratic cost functional. The smoothness term combines a standard first-order measure for image regions with a total-variation based measure for signal transitions. Accordingly, the costs associated with discontinuities are given by the length of level lines and local image contrast. For real images, this provides a reasonable approximation of the variational model of Mumford and Shah that has been suggested as a generic approach to image segmentation.The global properties of the convex variational model are favorable to applications: Uniqueness of the solution, continuous dependence of the solution on both data and parameters, consistent and efficient numerical approximation of the solution with the FEM-method.Various global and local properties of the convex variational model are analyzed and illustrated with numerical examples. Apart from the favorable global properties, the approach is shown to provide a sound mathematical model of a useful locally adaptive smoothing process. A comparison is carried out with results of a region-growing technique related to the Mumford-Shah model.     

基于单片机的高精度快速积分型AD转换器

针对现有积分型AD转换器存在转换速率较低的问题,提出了一种基于单片机的高精度快速积分型AD转换器的设计方案,详细介绍了AD转换的实现过程。该转换器采用单片机产生各种控制信号,保证了测量过程的精确同步,提高了AD测量结果的稳定性,最快转换速度可达500次/s。实际应用表明,该转换器转换速度快,测量精度高,达到了预期效果。     

变速风力发电机组出功控制策略研究

变速风力发电机组通常采用转速控制和变桨距控制相结合的方法,在额定风速以下主要调节发电机的转矩使转速跟随风速变化而变化,以获得最佳叶尖速比;在额定风速以上通过调整桨叶节距,改变气流对叶片攻角,从而改变风力发电机组获得的空气动力转矩,才能使功率输出保持稳定传统PID控制方案在风电控制中仍然发挥重要的作用,先进的控制算法与传...     

一类积分不等式的机器判定

将一类积分不等式转化为Tarski模型外的齐次对称多项式不等式,该类齐次对称多项式的次数是给定的,变元个数可以是任意多个,并且多项式的系数是与变元个数相关的变系数.这些特点与杨路等人最近提出的几个公开问题密切相关,是比较有代表性的一类齐次对称多项式.然后利用Timofte关于对称多项式不等式判定的降维方法,结合不等式证明软件BOTTEMA及差分代换方法,给出对应的一类Tarski模型外的齐次对称多项式不等式的机器判定算法,从而实现原积分不等式的机器判定.当给定的积分不等式及齐次对称多项式不等式不成立时,可给出具体不成立的数值反例.应用例子表明问题的广泛性及算法的有效性.     

一类控制量非负的新型脉冲随机控制模型

提出并研究了一类新型的脉冲随机控制模型,其状态结构由关于半鞅的线性随机微分方程所确定,其控制费用函数为关于控制前状态与控制量的二元函数且其控制量保持非负.首先建立了一类新型的变分方程并证明了其解的存在性.通过对变分方程的解函数进行一系列随机分析处理,证明了最佳控制的存在性且对其结构进行了深入分析.此外,由于本文模型与以往文献中的随机控制模型有着重大差异,因而在分析手法上与以往文献相比颇多差异之处.可以预期,本文不仅在随机控制的研究中将具有重要的理论意义,而且在金融控制及证券管理方面也将有着广泛的应用价值.     

A new nonlinear classifier with a penalized signed fuzzy measure using effective genetic algorithm

This paper proposes a new nonlinear classifier based on a generalized Choquet integral with signed fuzzy measures to enhance the classification accuracy and power by capturing all possible interactions among two or more attributes. This generalized approach was developed to address unsolved Choquet-integral classification issues such as allowing for flexible location of projection lines in n-dimensional space, automatic search for the least misclassification rate based on Choquet distance, and penalty on misclassified points. A special genetic algorithm is designed to implement this classification optimization with fast convergence. Both the numerical experiment and empirical case studies show that this generalized approach improves and extends the functionality of this Choquet nonlinear classification in more real-world multi-class multi-dimensional situations.     

Two-step design of critical control systems using disturbance cancellation integral controllers

An efficient critical control system design is proposed in this paper. The key idea is to decompose the design problem into two simpler design steps by the technique used in the classical loop transfer recovery method (LTR). The disturbance cancellation integral controller is used as a basic controller. Since the standard loop transfer recovery method cannot be applied to the disturbance cancellation controller, the nonstandard version recently found is used for the decomposition. Exogenous inputs with constraints both on the amplitude and rate of change are considered. The majorant approach is taken to obtain the analytical sufficient matching conditions. A numerical design example is presented to illustrate the effectiveness of the proposed design.