多Agent群体行为中的扩散趋同综述

为了研究复杂系统及复杂性科学问题,人们提出多Agent模型进行建模和分析.多Agent的群体行为是复杂系统研究所必须解决的问题,而其中的扩散趋同更是相关领域的研究热点.文中首先介绍多Agent群体行为中的扩散趋同现象,然后根据Agent扩散趋同传递的方式、Agent的扩散趋同能力的分布情况和Agent扩散趋同所感知的范围,将相关研究成果中的扩散趋同机制分为3个方面:层次结构性扩散趋同与群集性扩散趋同、平滑扩散趋同与非平滑扩散趋同、邻域扩散趋同与全局扩散趋同.文中对每种模型的特点进行论述和比较分析,最后指出下一步的研究方向.     

Use Hausdorff metric to analyze convergence of parameter estimation in system identification

For a linear time-invariant system model, this paper analyzes the convergence of parameter estimations as the length of the input–output data tends to infinity through the prediction error method. It is known that the sequence of the criterion functions converges uniformly in the parameter with probability one as the data length tends to infinity. The parameter estimation is represented by a set in general, instead of by a single point, on which the criterion function takes its minimum. Thus a mathematical feature of the convergence problem of parameter estimation is in that we are needed, from the convergence of a sequence of functions, to infer the convergence of the sequence of their sets of minimizing arguments. The Hausdorff metric is suggested to measure the distance between sets and then is used to discuss the convergence problem here. According to the Hausdorff metric, the convergence of parameter estimation is not guaranteed in general. A condition guaranteeing such convergence is given.     

A novel optimal PID controller autotuning design based on the SLP algorithm

A novel optimal proportional integral derivative (PID) autotuning controller design based on a new algorithm approach, the “swarm learning process” (SLP) algorithm, is proposed. It improves the convergence and performance of the autotuning PID parameter by applying the swarm and learning algorithm concepts. Its convergence is verified by two methods, global convergence and characteristic convergence. In the case of global convergence, the convergence rule of a random search algorithm is employed to judge, and Markov chain modelling is used to analyse. The superiority of the proposed method, in terms of characteristic convergence and performance, is verified through the simulation based on the automatic voltage regulator and direct current motor control system. Verification is performed by comparing the results of the proposed model with those of other algorithms, that is, the ant colony optimization with a new constrained Nelder–Mead algorithm, the genetic algorithm (GA), the particle swarm optimization (PSO) algorithm, and a neural network (NN). According to the global convergence analysis, the proposed method satisfies the convergence rule of the random search algorithm. With respect to the characteristic convergence and performance, the proposed method provides a better response than the GA, the PSO, and the NN for both control systems.     

保留精英遗传算法收敛性和收敛速度的鞅方法分析

论文引入鞅方法取代传统的马尔科夫链理论,研究保留精英遗传算法(EGA)的收敛条件和收敛速度.通过把EGA的最大适应值函数过程描述为下鞅,基于下鞅收敛定理构造使算法满足几乎处处收敛的充分条件,分析了概率1收敛充分条件与算法操作参数的关系,并计算了EGA获得全局最优解所需的最大进化代数.使用鞅方法分析遗传算法收敛性具有独特的优势,成为分析遗传算法收敛性及其性能的新方法.     

混合差分进化算法

为了克服差分进化算法容易出现早熟和收敛速度慢的问题,提出了一种混合差分进化算法.该算法在趋药性差分进化算法(CDE)的基础上,通过对较优个体进行变异操作,维护了种群多样性、避免早熟;通过将较差的个体与较优个体进行杂交,提高了开采能力、加快了收敛速度.基于这两种策略,算法的开采能力与探索能力达到了平衡.用该算法解决标准函数优化问题,并将仿真结果与其他算法进行比较,数值结果表明该文算法具有较快的收敛速度和很强的跳出局部最优的能力.     

Blind channel identification via stochastic approximation: constant step-size algorithms

This work is devoted to the study of a class of recursive algorithms for blind channel identification. Using weak convergence methods, the convergence of the algorithm is obtained and the rate of convergence is ascertained. The technique discussed can also be used in the analysis of rates of convergence for decreasing step-size algorithms.     

Convergence indicator: the case of cloud computing

Convergence is a term that is often used to explain changes in contemporary society, and technological convergence has been an important source of technological innovation in industry. For this paper we adopted a microscopic approach to the question of measuring the level of technological convergence using patent-citation analysis. We developed a convergence indicator that shows the relative convergence degree of a patent. This indicator, which is based on backward and forward patent citations, can assess the extent of the level of convergence or universality. Finally, we conducted an empirical analysis of the cloud computing area using patent-citation analysis from a microscopic perspective.     

The modes of convergence in the approximation of fuzzy random optimization problems

To develop the approximation approach to fuzzy random optimization problems, it is required to introduce the modes of convergence in fuzzy random theory. For this purpose, this paper first presents several novel convergence concepts for sequences of fuzzy random variables, such as convergence in chance, convergence in distribution and convergence in optimistic value; then deals with the convergence criteria and convergence relations among various types of convergence. Finally, we deal with the convergence theorems for sequences of integrable fuzzy random variables, including dominated convergence theorem and bounded convergence theorem.     

Convergence of multi-objective evolutionary algorithms to a uniformly distributed representation of the Pareto front

In evolutionary multi-objective optimization (EMO), the convergence to the Pareto set of a multi-objective optimization problem (MOP) and the diversity of the final approximation of the Pareto front are two important issues. In the existing definitions and analyses of convergence in multi-objective evolutionary algorithms (MOEAs), convergence with probability is easily obtained because diversity is not considered. However, diversity cannot be guaranteed. By combining the convergence with diversity, this paper presents a new definition for the finite representation of a Pareto set, the B-Pareto set, and a convergence metric for MOEAs. Based on a new archive-updating strategy, the convergence of one such MOEA to the B-Pareto sets of MOPs is proved. Numerical results show that the obtained B-Pareto front is uniformly distributed along the Pareto front when, according to the new definition of convergence, the algorithm is convergent.     

思维进化算法的转移概率分析及几乎处处收敛性证明

思维进化算法已有的收敛性分析均是在依概率收敛意义下考虑的,而几乎处处收敛强于依概率收敛。在详细分析思维进化算法趋同算子和异化算子转移概率的基础上,利用种群最大适应度值函数描述思维进化算法的演化过程,将最大适应度值函数的进化过程转化为下鞅数列,并根据数学期望的性质和最大适应度值函数的特点,利用下鞅收敛定理严格证明了思维进化算法的几乎处处收敛性。     

A proof of convergence of the concave-convex procedure using Zangwill's theory

The concave-convex procedure (CCCP) is an iterative algorithm that solves d.c. (difference of convex functions) programs as a sequence of convex programs. In machine learning, CCCP is extensively used in many learning algorithms, including sparse support vector machines (SVMs), transductive SVMs, and sparse principal component analysis. Though CCCP is widely used in many applications, its convergence behavior has not gotten a lot of specific attention. Yuille and Rangarajan analyzed its convergence in their original paper; however, we believe the analysis is not complete. The convergence of CCCP can be derived from the convergence of the d.c. algorithm (DCA), proposed in the global optimization literature to solve general d.c. programs, whose proof relies on d.c. duality. In this note, we follow a different reasoning and show how Zangwill's global convergence theory of iterative algorithms provides a natural framework to prove the convergence of CCCP. This underlines Zangwill's theory as a powerful and general framework to deal with the convergence issues of iterative algorithms, after also being used to prove the convergence of algorithms like expectation-maximization and generalized alternating minimization. In this note, we provide a rigorous analysis of the convergence of CCCP by addressing two questions: When does CCCP find a local minimum or a stationary point of the d.c. program under consideration? and when does the sequence generated by CCCP converge? We also present an open problem on the issue of local convergence of CCCP.     

On exponential convergence and robustness of least-squares identification

Robustness and convergence properties of exponentially weighted least-squares identification are studied. It is shown that exponential convergence in the noiseless case can be obtained for a class of increasing or decreasing regression vectors. The rate of change of the limits in the regressors affect the convergence rates, which are explicitly given. It is demonstrated that for a sub-class of regressors decreasing-in-the-norm exponential convergence without the noise does not guarantee robustness subject to a bounded noise. Instead, exponential divergence of the estimate is shown in a specific case.     

On the ideal convergence of double sequences in intuitionistic fuzzy normed spaces

Recently, the concept of statistical convergence of double sequences has been studied in intuitionistic fuzzy normed spaces by Mursaleen and Mohiuddine [Statistical convergence of double sequences in intuitionistic fuzzy normed spaces, Chaos, Solitons Fractals, 41 (2009) pp. 2414–2421]. We know that ideal convergence is more general than statistical convergence for single or double sequences. This has motivated us to study the ideal convergence of double sequences in a more general setting. That is, in this paper, we study the concept of ideal convergence and ideal Cauchy for double sequences in intuitionistic fuzzy normed spaces.     

A New Finite Time Convergence Condition for Super‐Twisting Observer Based on Lyapunov Analysis

A new convergence condition is proposed for the super‐twisting sliding mode observer in this paper, where Lyapunov stability analysis is used as the main method to get the new convergence condition. The super‐twisting sliding mode observer is designed to obtain unknown system states of the second order nonlinear system with bounded uncertainties and disturbances. By involving a quadratic Lyapunov function, the Lyapunov approach is applied to the stability analysis of the super‐twisting observer, from which a new convergence condition is obtained to guarantee the finite time convergence of the observer. Simulation results of a pendulum and a rigid manipulator are included to demonstrate the effectiveness of the new convergence condition.     

Improved convergence and speed for the distance geometry program APA to determine protein structure

The alternating projections algorithm (APA) is a program that is based on alternating projections and is designed to determine the three dimensional structure of proteins using distance geometry. The sampling and convergence properties of APA, as tested on bovine pancreatic trypsin inhibitor (BPTI) were previously reported in this journal. The results confirmed the program's earlier success with poly-L-alanine. However, the asymptotic convergence of the alternating projections was slow. We show that after good convergence is first obtained via alternating projections, that a weighted quadratic penalty function then produces the final conformation with better convergence in half the time required by the asymptotic convergence of alternating projections.     

Mean Shift算法的收敛性分析

作为迭代算法,Mean Shift的收敛性研究是应用的基础,而Comaniciu和李乡儒分别证明了Mean Shift的收敛性,但证明过程存在错误.首先指出了Comaniciu和李乡儒的证明过程存在错误;然后,从数学上重新证明了Mean Shift算法的局部收敛性,并指出其收敛到局部极大值的条件;最后,从几何上举反例分析了Mean Shift的收敛性,并进行了深入比较和讨论.这为Mean Shift算法的深入研究及应用奠定了基础.     

基于强化学习的多策略选择遗传算法

为解决传统遗传算法早熟收敛和收敛速度慢的问题,提出一种基于强化学习的多策略选择遗传算法MPSGA。通过使用不同的选择策略将整个种群划分为3个子种群并分别进化,能提高种群的多样性,有效避免遗传算法的早熟收敛问题。将种群的多样性和算法的运行机制相结合,根据种群多样性的变化运用强化学习算法动态地优化各子种群间的比例参数,从而将种群多样性保持在合适的范围,一定程度上解决了收敛速度和全局收敛性之间的矛盾。实验结果表明,该算法在收敛精度和搜索效率上都表现出较好的性能。     

On convergence properties of piecewise affine systems

In this paper convergence properties of piecewise affine (PWA) systems are studied. In general, a system is called convergent if all its solutions converge to some bounded globally asymptotically stable steady-state solution. The notions of exponential, uniform and quadratic convergence are introduced and studied. It is shown that for non-linear systems with discontinuous right-hand sides, quadratic convergence, i.e., convergence with a quadratic Lyapunov function, implies exponential convergence. For PWA systems with continuous right-hand sides it is shown that quadratic convergence is equivalent to the existence of a common quadratic Lyapunov function for the linear parts of the system dynamics in every mode. For discontinuous bimodal PWA systems it is proved that quadratic convergence is equivalent to the requirements that the system has some special structure and that certain passivity-like condition is satisfied. For a general multimodal PWA system these conditions become sufficient for quadratic convergence. An example illustrating the application of the obtained results to a mechanical system with a one-sided restoring characteristic, which is equivalent to an electric circuit with a switching capacitor, is provided. The obtained results facilitate bifurcation analysis of PWA systems excited by periodic inputs, substantiate numerical methods for computing the corresponding periodic responses and help in controller design for PWA systems.     

梯度微粒群优化算法及其收敛性分析

针对标准微粒群优化算法微粒运动轨迹的收敛性进行了分析.给出并证明了微粒运动轨迹收敛的充分条件.提出一种简便的等高线图判别法,该方法能够通过参数的位置判断微粒轨迹是否收敛并衡量收敛速度.为提高算法的收敛速度.构造出一种梯度微粒群优化算法,给出并证明了该方法收敛的充分条件.仿真结果表明,梯度微粒群优化算法具有优良的搜索性能.     

A Composite State Convergence Scheme for Bilateral Teleoperation Systems

State convergence is a novel control algorithm for bilateral teleoperation of robotic systems. First, it models the teleoperation system on state space and considers all the possible interactions between the master and slave systems. Second, it presents an elegant design procedure which requires a set of equations to be solved in order to compute the control gains of the bilateral loop. These design conditions are obtained by turning the master-slave error into an autonomous system and imposing the desired dynamic behavior of the teleoperation system. Resultantly, the convergence of master and slave states is achieved in a well-defined manner. The present study aims at achieving a similar convergence behavior offered by state convergence controller while reducing the number of variables sent across the communication channel. The proposal suggests transmitting composite master and slave variables instead of full master and slave states while keeping the operator’s force channel intact. We show that, with these composite and force variables; it is indeed possible to achieve the convergence of states in a desired way by strictly following the method of state convergence. The proposal leads to a reduced complexity state convergence algorithm which is termed as composite state convergence controller. In order to validate the proposed scheme in the absence and presence of communication time delays, MATLAB simulations and semi-real time experiments are performed on a single degree-of-freedom teleoperation system.