A cubic regularization algorithm for unconstrained optimization using line search and nonmonotone techniques

In recent years, cubic regularization algorithms for unconstrained optimization have been defined as alternatives to trust-region and line search schemes. These regularization techniques are based on the strategy of computing an (approximate) global minimizer of a cubic overestimator of the objective function. In this work we focus on the adaptive regularization algorithm using cubics (ARC) proposed in Cartis et al. [Adaptive cubic regularisation methods for unconstrained optimization. Part I: motivation, convergence and numerical results, Mathematical Programming A 127 (2011), pp. 245–295]. Our purpose is to design a modified version of ARC in order to improve the computational efficiency preserving global convergence properties. The basic idea is to suitably combine a Goldstein-type line search and a nonmonotone accepting criterion with the aim of advantageously exploiting the possible good descent properties of the trial step computed as (approximate) minimizer of the cubic model. Global convergence properties of the proposed nonmonotone ARC algorithm are proved. Numerical experiments are performed and the obtained results clearly show satisfactory performance of the new algorithm when compared to the basic ARC algorithm.     

精英改进粒子群算法在入库堆垛问题中的应用

针对钢铁企业生产与物流一体化协同管理中入库堆垛问题,基于出库次序A型约束、垛位选择分散性约束等,建立了以均衡库存垛位负载和最大化板坯综合匹配度为目标的联合优化模型。结合问题的特点,基于PSO算法,利用收敛指数判断种群进化状态,并对处于"收敛"状态的种群执行精英学习策略,提高粒子的活性,帮助种群跃出局部最优。最后通过实例仿真说明了模型与算法的有效性和可行性。     

Convergence Analysis of Wireless Remote Iterative Learning Control Systems with Channel Noise

Channel noise, including sensor‐to‐controller(SC) noise and controller‐to‐actuator(CA) noise, impacts the convergence of wireless remote iterative learning control (ILC) system significantly. In this paper, the relationship between output error, SC noise and CA noise is obtained firstly by super‐vector formulation, and then the norm of output error vector covariance matrix is employed to analyze the convergence of the system in presence of SC noise and CA noise. Upper bound of the norm at any sample time reveals that the SC noise is accumulated only in iteration domain, while the CA noise is accumulated not only in iteration domain but also in time domain. Furthermore, the accumulated effect of the CA noise in time domain is ruled by system matrices, so the values of which determine the effect of the CA noise is greater or less than that of the SC noise on convergence of the system. Finally, some simulation results are given to illustrate correctness of the result.     

Heuristic Dynamic Programming Using Echo State Network For Multivariable Tracking Control Of Wastewater Treatment Process

This paper proposes a heuristic dynamic programming (HDP) scheme to simultaneously control the dissolved oxygen concentration and the nitrate level in wastewater treatment processes (WWTP). Unlike traditional HDP schemes, the optimal control values are calculated in an analytical way by the proposed HDP controller. It can reduce the learning burden of the HDP controller to a great extent. The system model and the evaluation index J are approximated by two echo state networks (ESNs). Gradient‐based learning algorithms are employed to train both ESNs online, and the convergence of the training algorithm is investigated based on Lyapunov theory. The performance of the proposed ESN‐based HDP (E‐HDP) controller is tested and evaluated on a WWTP benchmark. Experimental results demonstrate that the proposed approach can achieve effective performance.     

一类非线性不确定系统的高阶积分自适应滑模控制

为有效改善传统积分滑模在一类非线性不确定系统的应用中存在的抖振问题,结合有限时间收敛理论、高阶积分滑模理论和自适应控制理论,提出了一种高阶积分自适应滑模控制方法;该方法通过选取有限时间收敛反馈量和设计高阶积分滑模面,不仅确保了系统状态误差在有限时间内的收敛性,而且还保证了系统具备对不确定性干扰的抑制能力;同时,当系统的不确定项的边界范围无法预先确定时,采用自适应方法对滑模控制的趋近速率进行估计;仿真结果表明所提方法能够有效抑制非线性不确定系统的抖动,并具有较高的控制精度.     

Optimal linear estimation for continuous stochastic systems with random observation delays

This paper is concerned with the linear minimum mean square error estimation for Itô‐type differential equation systems with random delays, where the delay process is modeled as a finite‐state Markov chain. By first introducing a set of equivalent delay‐free observations and then defining two reorganized Markov chains, the estimation problem of random delayed systems is reduced to the one of delay‐free Markov jump linear systems. The estimator is derived by using the innovation analysis method based on the Itô differential formula. And the analytical solution to this estimator is given in terms of two Riccati differential equations that are of finite dimensions. Conditions for existence, uniqueness, and stability of the steady‐state optimal estimator are studied for time‐invariant cases. In this case, the obtained estimator is very easy to implement, and all calculation can be performed off line, leading to a linear time‐invariant estimator. Copyright © 2011 John Wiley & Sons, Ltd.     

差分进化算法马尔可夫链模型及收敛性分析

差分进化算法是一种基于种群差异的优化算法,主要应用于解决连续空间的优化问题。目前,研究人员主要在算法的改进和应用方面研究差分进化算法,很少从理论角度对其进行研究。为了分析差分进化算法的收敛性,定义优化个体、种群的状态转移,并提出种群的最优状态集合。根据差分进化算法的操作算子计算出个体的状态迁移概率,并证明种群状态序列是有限齐次马尔可夫链,进而建立差分进化算法的马尔可夫链模型;最后,证明差分进化算法无法保证全局收敛。理论研究结果表明,适当保证种群的多样性能够提高差分进化算法的性能。     

Stability Analysis for Delayed Neural Networks with Discontinuous Neuron Activations

This paper considers a class of delayed neural networks with discontinuous neuron activations. Based on the theory of differential equations with discontinuous right‐hand sides, some novel sufficient conditions are derived that ensure the existence and global exponential stability of the equilibrium point. Moreover, by adopting the concept of convergence in measure, convergence behavior for the output is discussed. The obtained results are independent of the delay parameter and can be thought of as a generalization of previous results established for delayed neural networks with Lipschtz continuous neuron activations to the discontinuous case. Finally, we give a numerical example to illustrate the effectiveness and novelty of our results by comparing our results with those in the early literature.     

A Reconfiguration Trial on the Platform of Allied Information for Wireless Converged Networks

PAl （platform of allied information） promotes persistent innovation and application of the wireless technologies among different institutes, based on open, safe and controllable network architecture. The platform architecture, unified interface, security mechanism, and reconfiguration are designed to achieve the convergence of various wireless experimental resources. In this paper, the reconfiguration mechanism is designed and a reconfiguration trial is implemented based on the PAI, to verify the ability of integrating experimental resources in the related units.     

Inter-MAC Green Path Selection for Heterogeneous Networks

Gigabit home networks represent a key technology to make the Future Internet success a reality. Consumers will require networks to be simple to install, without the need of any new wire and with green consideration. To make this network ubiquitous, seamless and robust, a technology independent MAC layer will ensure its global control and provide connectivities to any number of devices the user wishes to connect to it in any room. In order to make this vision comes true, substantial progress is required in the fields of protocol design and in systems architectures. For this goal, we introduce a new convergence layer denoted inter-MAC, which provides a common infrastructure to the home network, Among the inter-MAC functionalities, we focused the interest on the path selection mechanism including the green aspect. To improve quality of service, reduce energy consumption and radiofrequency devices number, we define a new path selection protocol based on several metrics. We present some preliminary results obtained on an experimental test bed.