Finite-horizon neuro-optimal tracking control for a class of discrete-time nonlinear systems using adaptive dynamic programming approach

In this paper, a finite-horizon neuro-optimal tracking control strategy for a class of discrete-time nonlinear systems is proposed. Through system transformation, the optimal tracking problem is converted into designing a finite-horizon optimal regulator for the tracking error dynamics. Then, with convergence analysis in terms of cost function and control law, the iterative adaptive dynamic programming (ADP) algorithm via heuristic dynamic programming (HDP) technique is introduced to obtain the finite-horizon optimal tracking controller which makes the cost function close to its optimal value within an ?-error bound. Three neural networks are used as parametric structures to implement the algorithm, which aims at approximating the cost function, the control law, and the error dynamics, respectively. Two simulation examples are included to complement the theoretical discussions.     

基于近似匹配的知识化制造系统自重构研究

为解决实际中不同制造模式的实现之间存在大量相似但又不完全相同的模块的自重构问题,提出基于近似匹配的知识化制造系统自重构的理论和算法.首先讨论知识化制造系统自重构中知识点的匹配度定义和性质;然后给出基于近似匹配的知识网自重构算法,并利用C#和SQL Sever 2000在.NET平台上开发出基于近似匹配的自重构使能工具;最后通过实例说明基于近似匹配的自重构技术的应用.     

Low-discrepancy sampling for approximate dynamic programming with local approximators

Approximate dynamic programming (ADP) relies, in the continuous-state case, on both a flexible class of models for the approximation of the value functions and a smart sampling of the state space for the numerical solution of the recursive Bellman equations. In this paper, low-discrepancy sequences, commonly employed for number-theoretic methods, are investigated as a sampling scheme in the ADP context when local models, such as the Nadaraya–Watson (NW) ones, are employed for the approximation of the value function. The analysis is carried out both from a theoretical and a practical point of view. In particular, it is shown that the combined use of low-discrepancy sequences and NW models enables the convergence of the ADP procedure. Then, the regular structure of the low-discrepancy sampling is exploited to derive a method for automatic selection of the bandwidth of NW models, which yields a significant saving in the computational effort with respect to the standard cross validation approach. Simulation results concerning an inventory management problem are presented to show the effectiveness of the proposed techniques.     

基于签名的近似垃圾邮件检测算法

针对垃圾邮件短小、一定时间内在网络上重复、大量地散发的特点,提出了基于签名的近似垃圾邮件检测算法（ASD）。该算法以句为基本单位,求取邮件所含的全部句子的摘要,垃圾邮件的近似检测转变为两个摘要集近似度的比较。通过与近似文本查询算法DSC、DSC-SS、I-Match的比较,ASD算法在近似垃圾邮件查询中,表现出样本集的存储空间大小适中、运算时问短、鲁棒性高、高准确率、高召回率的特征。     

A review of stochastic algorithms with continuous value function approximation and some new approximate policy iteration algorithms for multidimensional continuous applications

We review the literature on approximate dynamic programming, with the goal of better understanding the theory behind practical algorithms for solving dynamic programs with continuous and vector-valued states and actions and complex information processes. We build on the literature that has addressed the well-known problem of multidimensional (and possibly continuous) states, and the extensive literature on model-free dynamic programming, which also assumes that the expectation in Bellman’s equation cannot be computed. However, we point out complications that arise when the actions/controls are vector-valued and possibly continuous. We then describe some recent research by the authors on approximate policy iteration algorithms that offer convergence guarantees (with technical assumptions) for both parametric and nonparametric architectures for the value function.     

Approximate string matching for music analysis

Approximate pattern matching algorithms have become an important tool in computer assisted music analysis and information retrieval. The number of different problem formulations has greatly expanded in recent years, not least because of the subjective nature of measuring musical similarity. From an algorithmic perspective, the complexity of each problem depends crucially on the exact definition of the difference between two strings. We present an overview of advances in approximate string matching in this field focusing on new measures of approximation.     

Reflections on the evolution of implicit Navier–Stokes algorithms

This article gives a historical perspective on the evolution of implicit algorithms for the Navier–Stokes equations that utilize time or Newton linearizations and various forms of approximate factorization including alternating-direction, lower/upper, and symmetric relaxation schemes. A theme of the paper is how progress in implicit Navier–Stokes algorithms has been influenced and enabled by the introduction of characteristic-based upwind approximations, unstructured-grid discretizations, parallelization, and by advances in computer performance and architecture. Historical examples of runtime, problem size, and estimated cost are given for actual and hypothetical Navier–Stokes flow cases from the past 40 years.     

基于Vague集模糊推理的多评价指标模糊决策方法

目前在智能领域中对Vague集的研究已越来越广泛与深入,并运用于决策问题中,有学者已把Vague集用于多评价指标的模糊决策中,但其决策方法在某些时候却难以得到目标。为此,本文提出了一个基于Vague集模糊推理的多评价指标模糊决策方法。在这个方法中,从基于Vague集的模糊推理的观点来看待模糊决策问题。将评价指标和候选方案之间的关系用一组基于Vague集的推理规则来表示,将决策者的要求用一组Vague集来表示,经过模糊推理等过程最后得到决策结果。然后还给出了一个实例说明这种多评价指标模糊决策方法。这个基于Vague集模糊推理的多评价指标模糊决策方法的提出为决策系统提供了一个有用的工具。