情报信息的判别分析网络计算软件研究

判别分析是多变量统计学的一种方法,可对样品进行类型的判定。在本研究中,研制了逐步判别分析的网络计算软件StepDiscrAnaly,给定变量入选和剔除的F临界值后,可得到判别函数及各样品的类别判定结果等。StepDis-crAnaly用Java网络化实现,可在兼容Java的浏览器上调用和运行,可用于情报信息的识别与检测研究。     

选择网络节点与通道的Java决策树计算

决策树是一种随机性决策方法.在决策树方法中,以期望收支值作为决策目标,根据可能出现的状态及概率,确定最优行动方案.本文给出了决策树的Java网络算法Decision_Tree,可在兼容Java的网络浏览器上调用和运行.决策树Java网络算法可用于网络中节点与通道的搜索选择,有一定的应用价值.     

JavaPCA:情报信息压缩抽取分析的一种网络共享软件

在情报信息分析工作中，经常会遇到大批量数据要处理。数据量大、信息不确定性高扰乱了信息可呈现的规律。从而需要对数据信息进行压缩，去除虚假信息，从压缩后的数据中抽提出所隐藏的规律。本文研制了用于情报信息压缩抽取分析的一种网络共享软件JavaPCA，该软件由6个Java类和一个HTML文件组成，有图型用户界面，可在多种Java兼容网络测览器如Internet Explorer和Netscape上运行。     

多变量情报信息的统计假设检验网络软件研究

多变量统计方法可用于分析多变量的情报信息。本研究给出了多变量假设检验的网络软件MultiVarHy-poTest,用于对多个多变量正态总体的均值、协方差阵等进行假设检验分析。MultiVarHypoTest用Java实现,图形用户界面友好,可在兼容Java的网络浏览器上调用和运行。     

一种新型异构数据信息整合与分析系统的构建

基于XML和Java技术,构建了异构数据信息整合与分析系统JavaXML,lavaXML将关系数据库和Native XML数据库相结合作为后台数据库,可存储和管理收集到的异构数据信息;利用JSp技术构建网络数据库,以XML形式的半结构化数据进行信息动态发布.JavaXML包含了一个数据挖掘与分析平台,该平台提供了多种数值与统计计算方法,如显著性标记的聚类分析.显著性标记的相关分析,模式识别算法等等.本平台在动态数据库访问、计算方法、运行环境、即时更新与跨平台等方面有明显优点.JavaXML具有灵活性、可移植性、高效性和轻便性等特点.有利于方便、快捷地整合与分析各种异构数据信息资源,为实现异构数据信息的存储、检索、传输和分析提供了一种新的解决方案.     

JavaPCA:情报信息压缩抽取分析的一种网络共享软件

本文研制了用于情报信息压缩抽取分析的一种网络共享软件JavaPCA，该软件由6个Java类和一个HTML文件组成，有图形用户界面，可在多种Java兼容网络浏览器如Internet Explorer和Netscape上运行。     

Java+XML--明智的选择 战略的决策

全球滚滚Java热 自SUN公司于1995年5月正式发布Java语言以来,她经历了初生、成长、成熟壮大的阶段,已经发展成为IT领域里的主流计算模式,从高性能计算,到移动计算(手机通信)、智能卡,都无处不体现出Java的存在,她已从当初的只是一种网络编程语言,逐渐发展成为一种网络通信技术,来实现全世界范围内的信息资源的共享和远程的交互通讯,由于它具有能在各种平台下运行的特点,又是具有面向对象技术的特征,能实现对动态画面进行控制的功能,以及高效的编程能力,使得她逐渐发展成为一种主流计算模式,其应用面已经超过了C++等传统语言,统计资料表明,全球超过85%的大型企业正在用Java开发自己的信息系统,业界公认:Java已进入主流计算模式.     

Java XML——明智的选择 战略的决策

全球滚滚Java热 　　自SUN公司于1995年5月正式发布Java语言以来,她经历了初生、成长、成熟壮大的阶段,已经发展成为IT领域里的主流计算模式,从高性能计算,到移动计算(手机通信)、智能卡,都无处不体现出Java的存在,她已从当初的只是一种网络编程语言,逐渐发展成为一种网络通信技术,来实现全世界范围内的信息资源的共享和远程的交互通讯,由于它具有能在各种平台下运行的特点,又是具有面向对象技术的特征,能实现对动态画面进行控制的功能,以及高效的编程能力,使得她逐渐发展成为一种主流计算模式,其应用面已经超过了C 等传统语言,统计资料表明,全球超过85%的大型企业正在用Java开发自己的信息系统,业界公认:Java已进入主流计算模式. 　　……     

基于PSO和滚动优化的不确定环境下移动机器人动态路径规划

针对全局环境未知且存在动态障碍物情况下的移动机器人路径规划问题,本文提出了一种结合粒子群算法(PSO)和滚动优化策略的动态路径规划方法。通过在一系列移动空间窗口中进行在线规划来充分利用机器人实时测得的局部环境信息,并用粒子群算法求解每一个移动窗口内的最优路径。为及时躲避动态障碍物,提出了一种适用于动态未知环境下的适应度函数。仿真试验表明,该方法克服了现有局部路径规划方法的高复杂性的缺点,算法操作简单、具有全局寻优能力、收敛速度快、鲁棒性好,可以满足机器人在复杂的未知动态环境下路径规划的实时性要求。     

情报信息因果分析的多变量回归模型网络软件MultiVarRegr

统计分析理论是信息分析的重要基础,其中,回归模型可描述变量之间的定量因果关系,已得到了广泛应用.在情报信息研究中,可对文献资料、期刊发行、网络资源等进行回归,相关和预测分析.本研究开发了多变量回归模型的网络软件MultiVarRegr,适用于多因变量-多自变量、多因变量-单自变量、单因变量-多自变量及单因变量-单自变量的回归分析.该软件用程序设计工具Java开发,图型用户界面友好,简单易用,驻留于远程服务器,可在多种兼容Java的网络浏览器,如Internet Explorer、HotJava、NescapeNavigator上调和运行,不仅可独立使用,也可以作为网络软件包及网络数据库的嵌入程序.     

基于提前支付强度过程的抵押贷款定价与均衡

基于提前支付强度过程考察了固定利率抵押贷款合同的定价和市场均衡问题.将均衡问题描述成代表性抵押人与市场之间的博弈.均衡由市场决定的内生抵押贷款利率和抵押人的最优再融资策略描述.在时齐Markov链利率及正线性比例再融资成本假设下,抵押人的规划问题可以简化成一个仅包含三个离散状态变量的Markov决策链,且一定存在唯一解.从而,均衡可由一个抵押利率决定函数和抵押人的最优再融资策略组成.一个简单的数值例子说明了计算均衡的迭代算法.结果表明,抵押人选择再融资往往是不明智的短视行为.     

Event-triggered-based adaptive dynamic programming for distributed formation control of multi-UAV

This paper is concerned with the distributed formation control problem of multi-quadrotor unmanned aerial vehicle (UAV) in the framework of event triggering. First, for the position loop, an adaptive dynamic programming based on event triggering is developed to design the formation controller. The critic-only network structure is adopted to approximate the optimal cost function. The merit of the proposed algorithm lies in that the event triggering mechanism is incorporated the neural network (NN) to reduce calculations and actions of the multi-UAV system, which is significant for the practical application. What’s more, a new weight update law based on the gradient descent technology is proposed for the critic NN, which can ensure that the solution converges to the optimal value online. Then, a finite-time attitude tracking controller is adopted for the attitude loop to achieve rapid attitude tracking. Finally, the efficiency of the proposed method is illustrated by numerical simulations and experimental verification.     

Event-triggered optimal interaction control of the MRM system under the complex multi-task constraints: Design and experiments

The main challenges of modular robot manipulators (MRMs) with the environmental constraints include the avoidance of catastrophic collision and the precious contacting in the whole interaction process. Consequently, an event-triggered optimal interaction control method of MRMs under the complex multi-task constraints is presented in this paper. Firstly, on the basis of the joint torque feedback (JTF) technique, the dynamic model of constrained MRM subsystem is established. Secondly, the sensorless-based decentralized nonlinear disturbance observer (NDOB) is proposed to detect and identify the sudden external collision for each joint. Then, the performance index function is improved to achieve the interaction control, which contains the fusion state variable function, the influence of external collision, the known model term, and the estimation of model uncertainties through the radial basis function neural network (RBFNN) identifier. Further, based on event-triggered mechanism and adaptive dynamic programming (ADP) algorithm, the approximate event-triggered optimal interaction control strategy is acquired by the critic neural network (NN). Next, the closed-loop MRM system is demonstrated to be uniformly ultimately bounded (UUB) through the Lyapunov stability theorem. Finally, the experiments are achieved effectively for each joint on the platform, such that the feasibility and universality of the proposed interaction control approach are testified by the experimental results.     

General value iteration based single network approach for constrained optimal controller design of partially-unknown continuous-time nonlinear systems

In this paper, a novel iterative approximate dynamic programming scheme is proposed by introducing the learning mechanism of value iteration (VI) to solve the constrained optimal control problem for CT affine nonlinear systems with utilizing only one neural network. The idea is to show the feasibility of introducing the VI learning mechanism to solve for the constrained optimal control problem from a theoretical point of view, and thus the initial admissible control can be avoided compared with most existing works based on policy iteration (PI). Meanwhile, the initial condition of the proposed VI based method can be more general than the traditional VI method which requires the initial value function to be a zero function. A general analytical method is proposed to demonstrate the convergence property. To simplify the architecture, only one critic neural network is adopted to approximate the iterative value function while implementing the proposed method. At last, two simulation examples are proposed to validate the theoretical results.     

Distributed total-amount optimal coordinated control of multi-motors with input and output saturation

Specific to the double saturation constraints of input and output in multimotor network systems, an anti-windup control framework with distributed total-amount optimal coordination is constructed, and a new saturated super-twisting sliding mode control strategy is designed in this paper. First, a mathematical model of direct torque and flux control of a multipermanent magnet synchronous motor is established. Next, the consistency of the total amount and output saturation are taken as the constraint conditions. Considering the lowest total energy consumption, the optimal multi-axis total-amount coordinated allocation algorithm is designed on the basis of the Karush-Kuhn-Tucker (KKT) condition. Then, the input saturation is introduced into the dynamic integral part of the super-twisting algorithm. A new saturated super-twisting sliding mode tracking control algorithm is designed, and the barrier Lyapunov function is used to prove the input constraint. Finally, the Matlab/Simulink simulation and RT-LAB semi-physical experiments verify that the anti-windup control strategy of distributed total-amount optimal coordination can effectively solve the double saturation constraints of input and output.     

时变条件下的危险品运输与应急救援双层规划研究

危险品运输因其发生事故的后果危害性大,因此对于如何选择一条既能保证运输安全又能满足运输需求的最优路径已经成为一项非常重要的研究内容。在总结前人研究的基础上,提出在时变条件下考虑应急救援的危险品运输路径双层规划模型,同时考虑运输时间限制与出发等待时间弹性以及运输网络中的应急救援能力。通过算法求解与应用算例,结果表明:双层规划更好地描述了企业和政府之间的博弈决策,而且将应急救援因素作为路径选择的重要依据有利于寻求更符合实际的最优路径。     

Bidirectional delta files

A bidirectional delta file is a novel concept, introduced in this paper, for a two way delta file. Previous work focuses on single way differential compression called forwards and backwards delta files. Here we suggest to efficiently combine them into a single file so that the combined file is smaller than the combination of the two individual ones. Given the bidirectional delta file of two files S and T and the original file S, one can decode it in order to produce T. The same bidirectional delta file is used together with the file T in order to reconstruct S. This paper presents two main strategies for producing an efficient bidirectional delta file in terms of the memory storage it requires; a quadratic time, optimal, dynamic programming algorithm, and a linear time, greedy algorithm. Although the dynamic programming algorithm often produces better results than the greedy algorithm, it is impractical for large files, and it is only used for theoretical comparisons. Experiments between the implemented algorithms and the traditional way of using both forwards and backwards delta files are presented, comparing their processing time and their compression performance. These experiments show memory storage savings of about 25% using this bidirectional delta approach as compared to the compressed delta file constructed using the traditional way, while preserving approximately the same processing time for decoding.     

Dual ML-ADHDP method for heterogeneous discrete-time nonlinear multi-agent systems with unknown dynamics and time delay

This paper develops a new dual ML-ADHDP method to solve the optimal consensus problem (OCP) of a class of heterogeneous discrete-time nonlinear multi-agent systems (MASs) with unknown dynamics and time delay. A hierarchical and distributed control strategy is used to transform the original problem into nonlinear model reference adaptive control (MRAC) problems and an OCP of virtual linear MASs. For the nonlinear MRAC problems, a new multi-layer action-dependent heuristic dynamic programming (ML-ADHDP) method is developed to overcome the unknown dynamics and neural network estimation errors, which has higher control accuracy. In order to solve the OCP of virtual linear MASs and improve the convergence speed, a new multi-layer performance index is proposed. Then the ML-ADHDP method is used to solve the coupled Hamiltonian–Jacobi–Bellman equation and obtain the optimal virtual control. Theoretical analysis proves that the original MASs can achieve Nash equilibrium, and simulation results show that the developed dual ML-ADHDP method ensures better convergence speed and higher control accuracy of original MASs.     

非线性规划法在供水管网优化中的应用研究

文章分析了非线性规划法的基本原理,并在此基础上建立供水管网优化模型。通过非线性规划模型的理论分析和实例计算,验证其在工程实践中的应用价值。非线性规划法在供水管网优化中的应用研究,为进一步改进管网优化设计方法提供了新的思路。     

动态规划算法分析与研究

分析多阶段决策问题,总结动态规划的基本概念、原理以及解题。通过0－1背包问题的具体解题步骤,阐述动态规划算法一般解题思路。并分析常用经典算法在解决最优问题中的差异性,比较各自优缺点,探讨其研究方向。