约束归纳逻辑程序设计方法的研究

提出了一种新的约束归纳逻辑程序设计方法。该方法能够与自顶向下的归纳逻辑程序设计系统结合,通过在自顶向下归纳方法的一步特殊化操作中引入Fisher判别分析等方法,使得系统能够导出不受变量个数限制的多种形式的线性约束,在不需要用户诱导,不依赖约束求解器的情况下,学习出覆盖正例而排斥负例的含约束的Horn子句程序。     

归纳逻辑程序设计综述

归纳逻辑程序设计是由机器学习与逻辑程序设计交叉所形成的一个研究领域,是机器学习的前沿研究课题。该文首先从归纳逻辑程序设计的问题背景、类型划分和搜索程序子句三个方面介绍了归纳逻辑程序设计系统的概貌;然后结合实验室的相关研究工作,回顾了归纳逻辑程序设计研究的发展;之后介绍了归纳逻辑程序设计领域中需要深入研究的若干问题,并提出了新的解决思路;最后是总结,以引起读者对归纳逻辑程序设计领域研究的进一步关注。     

遗传归纳逻辑程序设计的个体编码生长现象

遗传归纳逻辑程序设计(GILP)的个体编码生长现象严重影响了算法的性能和规则的可读性．通过对变长编码的模式分析，解释了GILP的个体编码生长现象．并发现，若从初始种群开始添加长度惩罚项来解决个体编码生长问题，种群会出现退化现象．而采取在演化的初期不添加惩罚项，在种群的性状有了明显改善后再添加惩罚的策略，既可避免种群退化，又可有效解决个体编码生长问题．     

归纳逻辑程序设计中的优化问题研究

归纳逻辑程序设计的核心问题是如何从背景知识中优选谓词构造满足约束的归纳假设,按Ｏｃｃａｍ准则,满足约束的最精简归纳假设为优,但迄今归纳逻辑程序设计中精简归纳假设构造的计算复杂性尚未解决。     

SC-PROLOG解释系统中的约束逻辑程序设计方法

约束逻辑程序设计（ＣＬＰ）方法是提高ＰＲＯＬＯＧ语言效率的一种崭新方法，本文针对ＳＣ┐ＰＲＯＬＯＧ解释系统的实现介绍其相应设计思想，从域变量含义入手，提出了域及约束的存储方法以及约束机制的实现算法，是对逻辑设计方法研究的一点体会     

并发约束程序设计语言COPS及其执行模型

约束程序设计尤其是约束逻辑程序设计与并发约束程序设计在AI程序设计领域占据着越来越重要的位置。传统逻辑程序设计的基“计算即为定理证明”的计算风格虽获得了简洁优美的操作语义特性,但也付出了执行效率低的代价,当应用系统规模增大时,其性能严重下降以致崩溃。针对传统逻辑程序设计的这种可伸缩性问题,设计了一个基于并发约束程序设计概念的说明性语言COPS,旨在从语言设计与执行模型两方面降低说明性程序的不确定性,提高搜索与运行效率。在语言设计方面,通过引入确定性语言成分,避免不确定计算用于确定性目标所浪费的系统开销;在执行模型方面,在目标的并发穿叉执行与数据驱动的并发同步机制的基础上,实现“优先执行确定目标”策略与“最少假定”策略,作为约束传播的延伸,最大幅度地剪枝搜索空间,降低搜索复杂性。COPS提供的知识表示、推理与并发机制使其成为构造agent程序的理想语言。论文给出COPS语言的语法规范与执行模型的操作语义描述。     

归纳逻辑程序设计方法综述

归纳逻辑程序设计是机器学习与逻辑程序设计交叉所形成的一个研究领域,克服了传统机器学习方法的两个主要限制：即知识表示的限制和背景知识利用的限制,成为机器学习的前沿研究课题。首先从归纳逻辑程序设计的产生背景、定义、应用领域及问题背景介绍了归纳逻辑程序设计系统的概貌,对归纳逻辑程序设计方法的研究现状进行了总结和分析,最后探讨了该领域的进一步的研究方向。     

归纳逻辑程序设计综述

归纳逻辑程序设计(inductive logic programming, ILP)是以一阶逻辑归纳理论为基础,并以一阶逻辑为表达语言的符号规则学习方法. ILP学得的模型是易于理解的一阶逻辑符号规则,而非难以解释的黑箱模型;在学习中可以相对容易地显式利用以一阶逻辑描述的领域知识;学得模型能对领域中个体间的关系进行建模,而非仅仅对个体的标记进行预测. 然而,由于潜在假设空间巨大,进行高效学习有相当的困难.综述了ILP领域的研究情况,从不同一阶逻辑归纳理论的角度对主流的ILP方法做出了梳理.还介绍了近年来ILP基于二阶诱导推理理论的扩展、基于概率的扩展和引入可微构件的扩展.最后,介绍了ILP在实际任务中的代表性应用,探讨了ILP方法目前所遇到的挑战,并对其未来发展进行了展望.     

一种可扩展型CLP系统的统一建模方法

约束推理是人工智能中主要组成部分之一,可以解决实际优化调度和规划过程中的约束求解问题。这里在解释了约束逻辑程序设计的原理和过程基础上,打破封闭式约束逻辑程序设计系统,从软件工程上采用统一建模语言,提出一种新的开放的可扩展型约束逻辑程序设计结构系统。为实现可扩展的约束推理搜索系统,引进UML建模语言中用例图、类图和协作图。在建模基础上详细说明了可扩展约束逻辑程序设计中数学模型,搜索引擎和搜索驱动三者间的关系以及它们内部的工作内容。最后在描述系统结构后,提出了可扩展的内容。根据扩展因素,外界为满足更多的需要可扩展本系统的约束过滤器。     

选择策略对遗传归纳逻辑程序设计收敛性能的影响

采用遗传算法（GA）作为归纳逻辑程序设计（ILP）的搜索策略，可以提高ILP方法的鲁棒性和适应性，文章简要叙述了对作者提出的遗传归纳逻辑程序设计（GILP）算法作的改进，测试了选择策略对GILP算法收敛性能的影响，采用不同的选择策略不会影响算法的最终收敛结果，但会产生不同的选择压力，导致算法具有不同的收敛速率。     

Fast relational learning using bottom clause propositionalization with artificial neural networks

Relational learning can be described as the task of learning first-order logic rules from examples. It has enabled a number of new machine learning applications, e.g. graph mining and link analysis. Inductive Logic Programming (ILP) performs relational learning either directly by manipulating first-order rules or through propositionalization, which translates the relational task into an attribute-value learning task by representing subsets of relations as features. In this paper, we introduce a fast method and system for relational learning based on a novel propositionalization called Bottom Clause Propositionalization (BCP). Bottom clauses are boundaries in the hypothesis search space used by ILP systems Progol and Aleph. Bottom clauses carry semantic meaning and can be mapped directly onto numerical vectors, simplifying the feature extraction process. We have integrated BCP with a well-known neural-symbolic system, C-IL²P, to perform learning from numerical vectors. C-IL²P uses background knowledge in the form of propositional logic programs to build a neural network. The integrated system, which we call CILP++, handles first-order logic knowledge and is available for download from Sourceforge. We have evaluated CILP++ on seven ILP datasets, comparing results with Aleph and a well-known propositionalization method, RSD. The results show that CILP++ can achieve accuracy comparable to Aleph, while being generally faster, BCP achieved statistically significant improvement in accuracy in comparison with RSD when running with a neural network, but BCP and RSD perform similarly when running with C4.5. We have also extended CILP++ to include a statistical feature selection method, mRMR, with preliminary results indicating that a reduction of more than 90 % of features can be achieved with a small loss of accuracy.     

一种改进的具有非理想网络状况的网络控制系统分析与综合方法

提出一种改进的具有非理想网络状况, 如时变网络时延和丢包等的网络控制系统分析与综合方法. 在引入具有最新信号选择功能的逻辑零阶保持器和已有的网络系统模型基础上, 采用Lyapunov-Krasovskii泛函方法, 通过引入自由矩阵消除交叉项和利用函数的凸性进行等价变换, 得到保守性较小的效果. 实例表明上述方法的有效性.     

分布式并行约束归纳逻辑程序设计研究

CILP是关系数据挖掘的主要技术之一。为提高CILP系统的效率,提出了一种基于C3模型,元学习技术和主从式静态负载平衡策略的分布式并行CILP算法,并实现了一个基于COW机群结构的分布式并行CILP原型系统。实验表明该算法是高效的,能获得较好的负载平衡,较高的加速比和并行效率。     

宽带网中基于正则化模糊神经网络的连接接纳控制

提出了一种宽带网络中基于模糊神经网络的连接接纳控制（CAC）方法,结合了模糊逻辑的语言控制能力和神经网络的自学习能力,通过合理地选择输入语言变量和设计模糊规则学习结果,以使CAC对连接的接受／拒绝作出正确决定,并保证服务质量（QoS）。仿真结果表明,与现有的各种CAC相比,该方法可获得更高的资料利用率,更大的吞叶量和更低的信元丢失率。     

Term and variable selection for non-linear system identification

The purpose of variable selection is to pre-select a subset consisting of the significant variables or to eliminate the redundant variables from all the candidate variables of a system under study prior to model term detection. It is required that the selected significant variables alone should sufficiently represent the system. Generally, not all the model terms, which are produced by combining different variables, make an equal contribution to the system output and terms, which make little contribution, can be omitted. A parsimonious representation, which contains only the significant terms, can often be obtained without the loss of representational accuracy by eliminating the redundant terms. Based on these observations, a new variable and term selection algorithm is proposed in this paper. The term detection algorithm can be applied to the general class of non-linear modelling problems which can be expressed as a linear-in-the-parameters form. The variable selection procedure is based on locally linear and cross-bilinear models, which are used together with the forward orthogonal least squares (OLS) and error reduction ratio (ERR) approach to determine the significant terms and to pre-select the important variables for both time series and input–output systems. Several numerical examples are provided to illustrate the applicability and effectiveness of the new approach.     

约束逻辑编程中宽数据算术运算算法

提出了一种在利用约束逻辑编程生成RTL数据通路模拟矢量的方法中处理宽数据的新方法。该方法解决了现有CLP求解器所能处理的最大整数限制问题。该文设计并实现了宽数据加法、减法和乘法运算的分解算法，运算分解扩展了现有CLP的功能，使其能适应实际的数据通路设计。实验结果表明方法是有效的。     

Stabilization for state/input delay systems via static and integral output feedback

This paper addresses the static and integral output feedback stabilization problems of continuous-time linear systems with an unknown state/input delay. By combining an augmentation approach and the delay partitioning technique, criteria for static and integral output feedback stabilizability are proposed in terms of nonlinear matrix inequalities with a free parameter matrix introduced. These new characterizations possess a special structure, which leads to linearized iterative computation. The effectiveness and merits of the proposed approach are shown through numerical examples.     

一种利用函数链神经网络的传感器建模新方法

讨论基于函数链神经网络 (FLNN)的传感器建模新方法 ,其结构简单、使用灵活、建模容易 ,易于实时硬件实现。两个算例说明网络的训练和非线性逼近方法 ,显示出网络的自适应能力、学习能力 ,基于FLNN的传感器模型可同时实现温度补偿和非线性校正。实际上 ,利用这种模型可以跟踪补偿环境改变引起的传感器特性的各种变化 ,在测控系统中具有良好的应用前景。     

A new delay system approach to network-based control

This paper presents a new delay system approach to network-based control. This approach is based on a new time-delay model proposed recently, which contains multiple successive delay components in the state. Firstly, new results on stability and H_∞ performance are proposed for systems with two successive delay components, by exploiting a new Lyapunov-Krasovskii functional and by making use of novel techniques for time-delay systems. An illustrative example is provided to show the advantage of these results. The second part of this paper utilizes the new model to investigate the problem of network-based control, which has emerged as a topic of significant interest in the control community. A sampled-data networked control system with simultaneous consideration of network induced delays, data packet dropouts and measurement quantization is modeled as a nonlinear time-delay system with two successive delay components in the state and, the problem of network-based H_∞ control is solved accordingly. Illustrative examples are provided to show the advantage and applicability of the developed results for network-based controller design.