六维力/力矩传感器静态解耦算法的研究与应用

维间耦合是影响多维力/力矩传感器测量精度的一个主要因素。介绍了六维力/力矩传感器维间耦合的基本原理,在研究基于求解矩阵广义逆的静态解耦算法的基础上,提出了基于耦合误差建模的静态解耦算法。以实验室研制的六维力/力矩传感器为例进行标定实验,用两种解耦算法对其进行解耦计算。实验结果证明基于耦合误差建模的静态解耦算法的有效性。     

基于IFWA-ELM的六维力传感器解耦算法

针对六维力传感器的维间耦合严重影响测量精度的问题，本文提出了一种基于改进烟花算法优化极限学习机（IFWA-ELM）的解耦算法。首先，对烟花算法的爆炸半径、变异算子和选择策略进行改进，形成改进烟花算法（IFWA）。其次，采用改进烟花算法寻找极限学习机的最佳网络参数，解决极限学习机随机生成初始权值和阈值导致网络不稳定、隐含层神经元数量对网络性能影响较大的问题。为了验证算法的解耦性能，本文以应用于4500m深海机械臂的六维力传感器作为研究对象，采用最小二乘法（LS）、BP神经网络（BPNN）、极限学习机（ELM）和IFWA-ELM算法进行解耦实验。实验结果表明：IFWA-ELM算法具有较好的非线性解耦能力，解耦后Ⅰ类误差控制在0.27%以内，Ⅱ类误差控制在0.13%以内，有效提高了六维力传感器的测量精度。     

混合液温度流量解耦控制算法及系统虚拟

针对一种具体的混合液温度流量非线性控制问题，文中提出了一种解析方式的解耦控制算法。采用MatLab对算法的仿真表明，这种解耦控制算法具有良好的解耦和控制效果。为全面分析该算法，设计了交互式实时混合液温度流量解耦控制系统虚拟软件，该软件的使用结果说明，这种算法可以很好地解决这种解耦控制问题。     

基于RF-GA的六维力传感器解耦方法

多维力传感器的维间耦合问题严重影响了检测精度的提高。通过设计新型RF-GA（基于遗传算法的改进随机森林算法）解耦方法解决多维力信息的解耦问题，实现提高力传感器检测精度的目标。针对随机森林算法中含有大量子树，但每个子树的预测准度无法保证的问题，利用遗传算法对随机森林的子树进行筛选，保留优质子树，从而提高预测精度。以基于应变检测的六维力传感器为实验对象，将RF-GA算法运用到实际力信息解耦中，并通过解耦实验对RF-GA算法进行验证。与现有解耦算法相比，RF-GA解耦方法具有精度高、解耦时间短的优点，实验结果表明该算法能有效提高多维力传感器的解耦精度。     

基于神经网络的解耦控制方法

本文介绍了BP算法的基本原理及其实现步骤,并将BP算法应用于神经网络解耦器和PID神经网络的训练中,即本文中各个神经网络的训练算法均采用BP算法,提出了一种神经网络在线解耦控制算法,即将神经网络解耦和神经网络PID控制两者结合,对系统进行解耦控制。将解耦与控制结合,既避免了单独采用自适应PID控制时控制效果不佳的问题,又避免了单独采用解耦时原有控制器不能适应变化后的对象问题。最后对一组双输入双输出耦合系统进行了仿真研究。     

基于BP神经网络的多变量PID解耦控制

基于神经网络的智能PID控制策略，以经典的PID控制理论为基础，并通过具有多变量解耦控制自学习功能的神经网络参数整定来实现。本文给出了网络的结构和算法，示出了一组二元变量强耦合时变系统的实时仿真结果。通过计算机仿真证明，基于神经网络的PID控制具有良好的自学习和自适应解耦控制能力。该系统融解耦器和控制器于一体，易于实现，适用于非线性多变量系统的解耦控制。它使解耦后的系统具有较好的动态和静态性能，特别是当根据BP控制规律确定了网络连接权系数的初值时，还能使系统参数快速收敛。     

多输入多输出非线性不确定系统连续高阶滑模控制

针对一类MIMO非线性不确定系统,提出一种新的连续高阶滑模控制算法.引入状态反馈使得系统高阶滑模控制问题等效转换为多变量不确定积分链的有限时间稳定问题,首先针对标称系统设计有限时间到达连续控制律,实现系统状态快速收敛,然后采用多变量非解耦形式超螺旋算法克服系统不确定性,实现鲁棒性,最终使得系统控制作用连续、滑模抖振得以大大抑制.基于二次型Lyapunov函数证明系统的有限时间稳定性.针对三阶不确定系统有限时间稳定和气垫船圆形航迹跟踪问题分别进行了仿真,验证了所提算法的有效性、鲁棒性.     

神经网络的具有自适应动量和步长的伪牛顿算法

以单隐层的３层前向神经网络为基础，由自适应ＢＰ算法和牛顿优化算法导出了自适应步长和动量解耦的伪牛顿算法（ＱＮＡＤＳＭ）．该算法计算量小，收敛速度快．文中还给出了该算法的收敛性证明、算法的仿真结果及其它算法的比较结果，并对网络的训练及该算法的特点作了进一步的讨论．仿真结果表明ＱＮＡＤＳＭ算法是一种有效的工程实用算法．     

一类非线性奇异控制系统的块解耦

讨论了在正则状态下反馈作用下，一类非线性奇异控制系统的输入输出块解耦问题，首先给一种算法，然后利用该算法导出系统输入块解耦问题可解的充分必要条件。     

基于神经网络技术的解耦及容错解耦控制策略

本文基于神经网络技术，分别对解耦控制和容错解耦控制进行了方案和算法研究，最后以业馏塔为例进行了仿真。结果表明，本文提出的方案和算法是有效的。     

Comparing Combination Rules of Pairwise Neural Networks Classifiers

A decomposition approach to multiclass classification problems consists in decomposing a multiclass problem into a set of binary ones. Decomposition splits the complete multiclass problem into a set of smaller classification problems involving only two classes (binary classification: dichotomies). With a decomposition, one has to define a recombination which recomposes the outputs of the dichotomizers in order to solve the original multiclass problem. There are several approaches to the decomposition, the most famous ones being one-against-all and one-against-one also called pairwise. In this paper, we focus on pairwise decomposition approach to multiclass classification with neural networks as the base learner for the dichotomies. We are primarily interested in the different possible ways to perform the so-called recombination (or decoding). We review standard methods used to decode the decomposition generated by a one-against-one approach. New decoding methods are proposed and compared to standard methods. A stacking decoding is also proposed which consists in replacing the whole decoding or a part of it by a trainable classifier to arbiter among the conflicting predictions of the pairwise classifiers. Proposed methods try to cope with the main problem while using pairwise decomposition: the use of irrelevant classifiers. Substantial gain is obtained on all datasets used in the experiments. Based on the above, we provide future research directions which consider the recombination problem as an ensemble method.     

Fast estimation of discretization error for FE problems solved by domain decomposition

This paper presents a strategy for a posteriori error estimation for substructured problems solved by non-overlapping domain decomposition methods. We focus on global estimates of the discretization error obtained through the error in constitutive relation for linear mechanical problems. Our method allows to compute error estimate in a fully parallel way for both primal (BDD) and dual (FETI) approaches of non-overlapping domain decomposition whatever the state (converged or not) of the associated iterative solver. Results obtained on an academic problem show that the strategy we propose is efficient in the sense that correct estimation is obtained with fully parallel computations; they also indicate that the estimation of the discretization error reaches sufficient precision in very few iterations of the domain decomposition solver, which enables to consider highly effective adaptive computational strategies.     

Augmented neural networks and problem structure-based heuristics for the bin-packing problem

In this article, we report on a research project where we applied augmented-neural-networks (AugNNs) approach for solving the classical bin-packing problem (BPP). AugNN is a metaheuristic that combines a priority rule heuristic with the iterative search approach of neural networks to generate good solutions fast. This is the first time this approach has been applied to the BPP. We also propose a decomposition approach for solving harder BPP, in which subproblems are solved using a combination of AugNN approach and heuristics that exploit the problem structure. We discuss the characteristics of problems on which such problem structure-based heuristics could be applied. We empirically show the effectiveness of the AugNN and the decomposition approach on many benchmark problems in the literature. For the 1210 benchmark problems tested, 917 problems were solved to optimality and the average gap between the obtained solution and the upper bound for all the problems was reduced to under 0.66% and computation time averaged below 33?s per problem. We also discuss the computational complexity of our approach.     

解决文本聚类集成问题的两个谱算法

聚类集成中的关键问题是如何根据不同的聚类器组合为最终的更好的聚类结果. 本文引入谱聚类思想解决文本聚类集成问题, 然而谱聚类算法需要计算大规模矩阵的特征值分解问题来获得文本的低维嵌入, 并用于后续聚类. 本文首先提出了一个集成算法, 该算法使用代数变换将大规模矩阵的特征值分解问题转化为等价的奇异值分解问题, 并继续转化为规模更小的特征值分解问题; 然后进一步研究了谱聚类算法的特性, 提出了另一个集成算法, 该算法通过求解超边的低维嵌入, 间接得到文本的低维嵌入. 在TREC和Reuters文本数据集上的实验结果表明, 本文提出的两个谱聚类算法比其他基于图划分的集成算法鲁棒, 是解决文本聚类集成问题行之有效的方法.     

A C-tree decomposition algorithm for 2D and 3D geometric constraint solving

In this paper, we propose a method which can be used to decompose a 2D or 3D constraint problem into a C-tree. With this decomposition, a geometric constraint problem can be reduced into basic merge patterns, which are the smallest problems we need to solve in order to solve the original problem in certain sense. Based on the C-tree decomposition algorithm, we implemented a software package MMP/Geometer. Experimental results show that MMP/Geometer finds the smallest decomposition for all the testing examples efficiently.     

控制燃料受限下离散线性系统能控域的研究*

本文讨论控制燃料受限下离散线性系统能控域问题。文中籍助于矩量论方法与线性系统分解技巧得到了能控域的结构性定理,该定理除与系统的Jordan分解有关外,还与状态矩阵的特征值有关。此外,文中还给出了判别任给状态是否属于能控域的显式判别条件。这些结果在实际应用中是有用的。     

非线性控制系统与状态空间的几何结构

首行从整体化的观点定义了一种建立在黎曼流形上的非线性控制系统,给出了系统的状态方程在黎曼流形的局部坐标系下的表达式,讨论了黎曼流形的几何结构对非线性系统的影响,研究了非线性系统的能控性和能观测性,其次,利用对合分布与全测地子流形的性质,给出了建立在黎曼流形上的非线性系统的局部能控结构分解,局部能观结构分解和Kalman分解,第三,分别利用彼此正交的对合分布族和递增对合分布族与全测地子流形族的性质。研究了建立在黎曼流形上的非线性控制系统平等解耦问题和级联解耦问题,以及仿射非线性控制系统的局部干扰解耦问题。     

Decomposition methodology for classification tasks: a meta decomposer framework

The idea of decomposition methodology for classification tasks is to break down a complex classification task into several simpler and more manageable sub-tasks that are solvable by using existing induction methods, then joining their solutions together in order to solve the original problem. In this paper we provide an overview of very popular but diverse decomposition methods and introduce a related taxonomy to categorize them. Subsequently, we suggest using this taxonomy to create a novel meta-decomposer framework to automatically select the appropriate decomposition method for a given problem. The experimental study validates the effectiveness of the proposed meta-decomposer on a set of benchmark datasets.     

A reliable algorithm for obtaining positive solutions for nonlinear boundary value problems

In this paper, we propose an algorithm for solving the nonlinear two-point boundary value problem

that has at least one positive solution [1–6] for λ in a compatible interval. Our method stems mainly from combining the decomposition series solution obtained by Adomian decomposition method with Padé approximates. The validity of the approach is verified through illustrative numerical examples     

Problem decomposition in distributed problem-solving systems

Distributed Problem Solving (DPS) is defined as the cooperative solution of problems by a decentralized and loosely coupled collection of problem solvers (agents), each of them knowing how to execute only some of the necessary tasks. This approach considers the problem-solving process as occurring in three phases: problem decomposition, subproblem solution, and answer synthesis. In the problem decomposition phase, one has to determine which tasks will be executed by each agent and when. One of the key research questions in the problem decomposition process is how to decompose a problem in order to minimize the cost of resources needed for its solution. In this article, we construct mathematical programming models in order to describe the decomposition process under the above criterion, study its complexity, and present exact and heuristic algorithms for its solution. Our work was motivated by the operation of an actual system that can be considered as a distributed problem solver for the assessment of irrigation projects design.