基于K均值与WPA-RBF神经网络模拟电路故障诊断

针对模拟电路故障诊断进行了研究,提出了一种新的方法。该方法包括haar的小波分解,对数据的归一化处理,以及用K均值优化RBF的中心向量和宽度,用狼群算法优化RBF的权值。首先用haar小波对所得的电路原始故障数据集进行变换,然后对变换后的数据进行归一化处理,最终得出RBF神经网络训练所需的输入数据。针对RBF神经网络中隐层节点中心、基函数宽度及权值选取困难问题,这里用K均值优化RBF的中心向量和宽度,用狼群算法优化RBF的权值,以提高网络训练稳定性与诊断成功率。最终通过两个电路的诊断实例,来论述该方法的具体实现过程,验证用该方法进行模拟电路故障诊断的可行性。     

基于滚动时域的无人机动态航迹规划

针对带有动力学约束的多旋翼无人机航迹规划问题,提出了一种基于滚动时域控制和快速粒子群优化(RHC-FPSO)方法。该方法引入了基于VORONOI图的代价图方法说明从航迹端点到达目标点的距离估计。根据滚动时域和人工势场法的思想,将路径规划问题转化为优化问题,以最小距离和其他性能指标为代价函数。设计评价函数准则,按照评价准则使用变权重粒子群优化算法求解。针对无人机靠近危险区飞行的问题,将斥力场引入到代价函数中,提升其安全性。仿真实验结果显示,使用文中方法可以有效地在满足约束条件下穿过障碍物区域,以及在复杂环境下可以动态计算。     

A continuous optimization framework for hybrid system identification

We propose a new framework for hybrid system identification, which relies on continuous optimization. This framework is based on the minimization of a cost function that can be chosen as either the minimum or the product of loss functions. The former is inspired by traditional estimation methods, while the latter is inspired by recent algebraic and support vector regression approaches to hybrid system identification. In both cases, the identification problem is recast as a continuous optimization program involving only the real parameters of the model as variables, thus avoiding the use of discrete optimization. This program can be solved efficiently by using standard optimization methods even for very large data sets. In addition, the proposed framework easily incorporates robustness to different kinds of outliers through the choice of the loss function.     

基于DAGSVC的模拟电路故障字典法

针对模拟电路的故障诊断和支持向量机分类器的设计问题,讨论了一种基于有向无环图支持向量机分类器(DAGSVC)的故障字典新方法,并比较了几种支持向量机故障分类器的平均测试复杂度指标.通过对2个实际模拟滤波器的实际测试和验证表明:该方法性能要优于"1-v-r"SVC,"1-v-1"SVC等常规的故障分类器,并和聚类二叉树S...     

Mathematical models of trade-off schemes in multicriteria mathematical programming problems with fuzzy constraints

Single- and multicriteria mathematical programming problems with fuzzy constraints are formulated as vector optimization problems. The formulations of such problems, their mathematical models, trade-off schemes, efficiency criteria, and solution methods are considered. Fuzzy logic membership functions are proposed to represent losses due to violations of some boundary conditions. Methods for the normalization of local criteria are given. Algorithms and computational schemes are proposed to solve these problems where the solutions are chosen from a finite set of alternatives. A numerical example is given.     

发掘相关反馈日志中关联信息的图像检索方法

相关反馈日志蕴含着丰富的对象语义关联信息,但大多数基于内容的图像检索(CBIR)方法却缺乏对它们的重用.提出一种发掘反馈日志中图像关联信息的自动化图像检索方法,将反馈事例中图像的共生现象视为一定上下文中的图像分类.检索时,结合CBIR的检索结果和多种上下文中的图像分类实例,借鉴HITS算法的思想从中提炼图像的本质性关联,获得综合内容和语义的图像检索结果.对6万幅Corel图像数据库的实验表明,该方法可以显著改善查全率和查准率,且检索结果能够更好地满足用户的语义检索需求.     

QAOA最大切割问题的类Dijkstra优化及实现

最大切割问题是可以用量子近似优化算法（QAOA）来解决的典型问题,Ansatz线路构造为该算法的重要组成部分。为了减少多种图结构在QAOA中的构造代价和提高其稳定性,从线路的可优化性出发进行分析,结合 Dijkstra算法的点边存放特点,提出了该线路的类Dijkstra优化算法,并将其应用于QAOA最大切割问题。使用qiskit量子框架来模拟优化算法的正确性,并用IBM Quantum composer的真实环境进行对比实验来验证优化的稳定性。与未优化的线路相比,此优化算法下的CNOT门能减少约40%左右,其稳定性也得到了明显的提高。结果表明类Dijkstra优化算法可以适用于QAOA最大切割问题的多种图结构优化。     

Robust normalization and guaranteed cost control for a class of uncertain singular Markovian jump systems via hybrid impulsive control

This paper investigates the problem of robust normalization and guaranteed cost control for a class of uncertain singular Markovian jump systems. The uncertainties exhibit in both system matrices and transition rate matrix of the Markovian chain. A new impulsive and proportional‐derivative control strategy is presented, where the derivative gain is to make the closed‐loop system of the singular plant to be a normal one, and the impulsive control part is to make the value of the Lyapunov function does not increase at each time instant of the Markovian switching. A linearization approach via congruence transformations is proposed to solve the controller design problem. The cost function is minimized via solving an optimization problem under the designed control scheme. Finally, three examples (two numerical examples and an RC pulse divider circuit example) are provided to illustrate the effectiveness and applicability of the proposed methods. Copyright © 2013 John Wiley & Sons, Ltd.     

Evolutionary algorithms + domain knowledge = real-world evolutionary computation

We discuss implicit and explicit knowledge representation mechanisms for evolutionary algorithms (EAs). We also describe offline and online metaheuristics as examples of explicit methods to leverage this knowledge. We illustrate the benefits of this approach with four real-world applications. The first application is automated insurance underwriting-a discrete classification problem, which requires a careful tradeoff between the percentage of insurance applications handled by the classifier and its classification accuracy. The second application is flexible design and manufacturing-a combinatorial assignment problem, where we optimize design and manufacturing assignments with respect to time and cost of design and manufacturing for a given product. Both problems use metaheuristics as a way to encode domain knowledge. In the first application, the EA is used at the metalevel, while in the second application, the EA is the object-level problem solver. In both cases, the EAs use a single-valued fitness function that represents the required tradeoffs. The third application is a lamp spectrum optimization that is formulated as a multiobjective optimization problem. Using domain customized mutation operators, we obtain a well-sampled Pareto front showing all the nondominated solutions. The fourth application describes a scheduling problem for the maintenance tasks of a constellation of 25 low earth orbit satellites. The domain knowledge in this application is embedded in the design of a structured chromosome, a collection of time-value transformations to reflect static constraints, and a time-dependent penalty function to prevent schedule collisions.     

面向链接预测的知识图谱表示学习方法综述

作为人工智能的重要基石, 知识图谱能够从互联网海量数据中抽取并表达先验知识, 极大程度解决了智能系统认知决策可解释性差的瓶颈问题, 对智能系统的构建与应用起关键作用. 随着知识图谱技术应用的不断深化, 旨在解决图谱欠完整性问题的知识图谱补全工作迫在眉睫. 链接预测是针对知识图谱中缺失的实体与关系进行预测的任务, 是知识图谱构建与补全中不可或缺的一环. 要充分挖掘知识图谱中的隐藏关系, 利用海量的实体与关系进行计算, 就需要将符号化表示的信息转换为数值形式, 即进行知识图谱表示学习. 基于此, 面向链接预测的知识图谱表示学习成为知识图谱领域的研究热点. 从链接预测与表示学习的基本概念出发, 系统性地介绍面向链接预测的知识图谱表示学习方法最新研究进展. 具体从知识表示形式、算法建模方式两种维度对研究进展进行详细论述. 以知识表示形式的发展历程为线索, 分别介绍二元关系、多元关系和超关系知识表示形式下链接预测任务的数学建模. 基于表示学习建模方式, 将现有方法细化为4类模型: 平移距离模型、张量分解模型、传统神经网络模型和图神经网络模型, 并详细描述每类模型的实现方式与解决不同关系元数链接预测任务的代表模型. 在介绍链接预测的常用的数据集与评判标准基础上, 分别对比分析二元关系、多元关系和超关系3类知识表示形式下, 4类知识表示学习模型的链接预测效果, 并从模型优化、知识表示形式和问题作用域3个方面展望未来发展趋势.     

Optimizing the data-dependent kernel under a unified kernel optimization framework

The kernel functions play a central role in kernel methods, accordingly over the years the optimization of kernel functions has been a promising research area. Ideally Fisher discriminant criteria can be used as an objective function to optimize the kernel function to augment the margin between different classes. Unfortunately, Fisher criteria are optimal only in the case that all the classes are generated from underlying multivariate normal distributions of common covariance matrix but different means and each class is expressed by a single cluster. Due to the assumptions, Fisher criteria obviously are not a suitable choice as a kernel optimization rule in some applications such as the multimodally distributed data. In order to solve this problem, recently many improved discriminant criteria (DC) have been also developed. Therefore, to apply these discriminant criteria to kernel optimization, in this paper based on a data-dependent kernel function we propose a unified kernel optimization framework, which can use any discriminant criteria formulated in a pairwise manner as the objective functions. Under the kernel optimization framework, to employ different discriminant criteria, one has to only change the corresponding affinity matrices without having to resort to any complex derivations in feature space. Experimental results based on some benchmark data demonstrate the efficiency of our method.     

一个新的无向图画图算法

将一般无向图的画图问题转化为函数优化问题,用遗传算法求目标函数的最优解的近似值,从而得到无向图自动画图算法的一个一般框架.新方法的特点是:不同的画图算法的框架都一样,所不同的只是反映无向图画图问题的美观标准的目标函数.其优点在于,算法统一、方法简单、容易实现、便于修改,并且易于并行化,可以直接用来画非连通图.     

模拟集成器件在RF领域中的应用

模拟工艺技术的快速发展推动了模拟器件水平的极大提高,为模拟集成电路提供了巨大的发展空间.近年来,模拟集成电路在通讯领域中的发展最为讯猛,其中基于RF器件和电路的无线应用已发展为半导体制造业中非常重要的应用领域.文章主要针对RF领域,对用于模拟集成电路的CMOS和SiGe器件的发展动态及趋势作了分析.     

ESAD中解保电源信号识别优化方法

针对ESAD解除保险过程中,无法准确识别解保电源信号电压幅值的问题,提出了ESAD中解保电源信号识别优化 方法。该方法采用“数字量+模拟量”的识别方法,其中光耦隔离电路作为数字量识别方式,AD采集电路作为模拟量识别方式, “数字量+模拟量”信号共同作为解保条件,以模拟量识别值作为起始判断门限,数字量信号进行有效判断,可提高ESAD的作用 可靠性。验证试验表明,采用“数字量+模拟量”识别方法可有效识别解保电源信号且保证ESAD的作用可靠性,该方法具有可 行性。     

Evolutionary algorithms,simulated annealing and tabu search: a comparative study

Evolutionary algorithms, simulated annealing (SA), and tabu search (TS) are general iterative algorithms for combinatorial optimization. The term evolutionary algorithm is used to refer to any probabilistic algorithm whose design is inspired by evolutionary mechanisms found in biological species. Most widely known algorithms of this category are genetic algorithms (GA). GA, SA, and TS have been found to be very effective and robust in solving numerous problems from a wide range of application domains. Furthermore, they are even suitable for ill-posed problems where some of the parameters are not known before hand. These properties are lacking in all traditional optimization techniques. In this paper we perform a comparative study among GA, SA, and TS. These algorithms have many similarities, but they also possess distinctive features, mainly in their strategies for searching the solution state space. The three heuristics are applied on the same optimization problem and compared with respect to (1) quality of the best solution identified by each heuristic, (2) progress of the search from initial solution(s) until stopping criteria are met, (3) the progress of the cost of the best solution as a function of time (iteration count), and (4) the number of solutions found at successive intervals of the cost function. The benchmark problem used is the floorplanning of very large scale integrated (VLSI) circuits. This is a hard multi-criteria optimization problem. Fuzzy logic is used to combine all objective criteria into a single fuzzy evaluation (cost) function, which is then used to rate competing solutions.     

On a Kernel-Based Method for Pattern Recognition, Regression, Approximation, and Operator Inversion

We present a kernel-based framework for pattern recognition, regression estimation, function approximation, and multiple operator inversion. Adopting a regularization-theoretic framework, the above are formulated as constrained optimization problems. Previous approaches such as ridge regression, support vector methods, and regularization networks are included as special cases. We show connections between the cost function and some properties up to now believed to apply to support vector machines only. For appropriately chosen cost functions, the optimal solution of all the problems described above can be found by solving a simple quadratic programming problem. Received January 31, 1997; revised June 1, 1997, and July 7, 1997.     

射频锁相环电路工作特性仿真与分析

射频锁相环是一种能够跟踪输入信号相位变化的闭环自动跟踪系统,此类电路具有负反馈闭环特点、系统状态多,工作特性复杂,其故障诊断和定位是无线电设备维修保障中的难点问题。针对上述问题,研究了射频锁相环电路的工作特性与故障定位方法。首先,针对典型射频锁相环电路,推导得到了其数学模型。然后,建立了其电路仿真的模型,仿真分析了环路四种主要工作状态和各模块对环路整体性能的影响。最后,采用开环故障隔离与关键节点信号比对法进行环路故障定位。研究结果为模拟锁相环电路故障诊断提供了理论依据,对实际电路故障诊断具有一定参考价值。     

A Dynamic Diffusion Optimization Method for Irregular Finite Element Graph Partitioning

To efficiently execute a finite element application program on a distributed memory multicomputer, we need to distribute nodes of a finite element graph to processors of a distributed memory multicomputer as evenly as possible and minimize the communication cost of processors. This partitioning problem is known to be NP-complete. Therefore, many heuristics have been proposed to find satisfactory sub-optimal solutions. Based on these heuristics, many graph partitioners have been developed. Among them, Jostle, Metis, and Party are considered as the best graph partitioners available up-to-date. For these three graph partitioners, in order to minimize the total cut-edges, in general, they allow 3% to 5% load imbalance among processors. This is a tradeoff between the communication cost and the computation cost of the partitioning problem. In this paper, we propose an optimization method, the dynamic diffusion method (DDM), to balance the 3% to 5% load imbalance allowed by these three graph partitioners while minimizing the total cut-edges among partitioned modules. To evaluate the proposed method, we compare the performance of the dynamic diffusion method with the directed diffusion method and the multilevel diffusion method on an IBM SP2 parallel machine. Three 2D and two 3D irregular finite element graphs are used as test samples. For each test sample, 3% and 5% load imbalance situations are tested. From the experimental results, we have the following conclusions. (1) The dynamic diffusion method can improve the partition results of these three partitioners in terms of the total cut-edges and the execution time of a Laplace solver in most test cases while the directed diffusion method and the multilevel diffusion method may fail in many cases. (2) The optimization results of the dynamic diffusion method are better than those of the directed diffusion method and the multilevel diffusion method in terms of the total cut-edges and the execution time of a Laplace solver for most test cases. (3) The dynamic diffusion method can balance the load of processors for all test cases.     

Minimizing nonsubmodular functions with graph cuts - a review

Optimization techniques based on graph cuts have become a standard tool for many vision applications. These techniques allow to minimize efficiently certain energy functions corresponding to pairwise Markov random fields (MRFs). Currently, there is an accepted view within the computer vision community that graph cuts can only be used for optimizing a limited class of MRF energies (e.g., submodular functions). In this survey, we review some results that show that graph cuts can be applied to a much larger class of energy functions (in particular, nonsubmodular functions). While these results are well-known in the optimization community, to our knowledge they were not used in the context of computer vision and MRF optimization. We demonstrate the relevance of these results to vision on the problem of binary texture restoration.