高炉专家系统知识的实例学习*

知识获取是专家系统建造过程中最重要的环节之一，本文根据高炉炉况知识的特点，采用背景知识学习算法、归纳学习算法，实现了高炉专家系统知识的实例学习。     

Derandomizing Stochastic Prediction Strategies

In this paper we continue study of the games of prediction with expert advice with uncountably many experts. A convenient interpretation of such games is to construe the pool of experts as one stochastic predictor, who chooses one of the experts in the pool at random according to the prior distribution on the experts and then replicates the (deterministic ) predictions of the chosen expert. We notice that if the stochastic predictor's total loss is at most L with probability at least p then the learner's loss can be bounded by cL + aln for the usual constants c and a. This interpretation is used to revamp known results and obtain new results on tracking the best expert. It is also applied to merging overconfident experts and to fitting polynomials to data.     

Improved expert selection model for forex trading

Online prediction is a process that repeatedly predicts the next element in the coming period from a sequence of given previous elements. This process has a broad range of applications in various areas, such as medical, streaming media, and finance. The greatest challenge for online prediction is that the sequence data may not have explicit features because the data is frequently updated, which means good predictions are difficult to maintain. One of the popular solutions is to make the prediction with expert advice, and the challenge is to pick the right experts with minimum cumulative loss. In this research, we use the forex trading prediction, which is a good example for online prediction, as a case study. We also propose an improved expert selection model to select a good set of forex experts by learning previously observed sequences. Our model considers not only the average mistakes made by experts, but also the average profit earned by experts, to achieve a better performance, particularly in terms of financial profit. We demonstrate the merits of our model on two real major currency pairs corpora with extensive experiments.     

如何用Jess开发专家系统

在当今的人工领域当中，出现了许多以Java为核心技术的专家系统开发工具，Jess(Java expert system shell)就是一种基于Java的专家系统外壳，本文简要介绍了Jess的内容，并给出了用它开发专家系统的一些基本的应用方法。     

Expert maintenance systems in telecommunication networks

Our survey of some 40 network maintenance expert systems reveals theri main shortcoming, which is the difficulty to acquire troubleshooting knowledge both when initializing the expert system and after its deployment. Additionally, the state-of-the-art troubleshooting expert systems do not optimize troubleshooting cost. We present theAO ^* algorithm to generate a network troubleshooting expert system which minimizes the expected troubleshooting cost and learns better troubleshooting techniques during its operation.     

专家控制及其在微型反应器中的应用

本文介绍了专家控制的基本原理和系统结构，分析了专家控制与传统控制技术的关系，采用专家控制技术实现了一类微型反应器温度控制，并取得了很好的结果。     

基于专家控制器技术的抽油机系统

介绍了一种基于专家控制器设计思想的抽油控制系统,重点讨论了专家控制器系统的工作原理以及软件系统设计和实现中的几个其它技术问题。     

利用学习向量化样本分类的在线学习成绩预测

对网络在线学习者产生的数据进行记录和分析,并为其提供精准化的个性化服务是在线教育发展的重要方面.本文以学习者在平台上产生的日常学习数据为样本,综合其最具代表性的五种影响因子,通过学习向量化神经网络对样本进行分类,得到基于BP神经网络的在线学习成绩预测数据.在模型中采用遗传算法有效优化BP神经网络的权重和阈值,在提高预测精度的同时加快模型的收敛速度.最后与其他两种模型进行对比分析,结果表明：该模型进行预测的结果与真实的成绩分布基本一致,具有很高的可信度,能够为有效的预测学习状态提供决策依据,具有一定的工程应用价值.     

Learning dynamic algorithm portfolios

Algorithm selection can be performed using a model of runtime distribution, learned during a preliminary training phase. There is a trade-off between the performance of model-based algorithm selection, and the cost of learning the model. In this paper, we treat this trade-off in the context of bandit problems. We propose a fully dynamic and online algorithm selection technique, with no separate training phase: all candidate algorithms are run in parallel, while a model incrementally learns their runtime distributions. A redundant set of time allocators uses the partially trained model to propose machine time shares for the algorithms. A bandit problem solver mixes the model-based shares with a uniform share, gradually increasing the impact of the best time allocators as the model improves. We present experiments with a set of SAT solvers on a mixed SAT-UNSAT benchmark; and with a set of solvers for the Auction Winner Determination problem. This work was supported by SNF grant 200020-107590/1.     

Qualitative Event-Based Expert Supervision for Transient Condition Monitoring

This paper addresses the following supervisory problem: a continuous plant (P) is to be supervised via symbolic (or quantised) actions. These symbolic actions suggest the set points for the lower level control loops. The system dynamic is analysed on the supervisory level (K) by a qualitative approach. The relationships between variables and the steady-state references are known. These problems are especially common in chemical process control. The supervisor handles start-up and shut-down procedures and takes appropriate action to solve the sequential or parallel tasks of a basic procedure. The object of this paper is to introduce an approach to solving the problem of how to derive a set of rules from a physical process.The solutions for supervising start-up and shut-down operations in close loop are suitable for large industrial systems, as are as the batch and semi-continuous processes used in order to maintain operations in a dynamic mode. This paper considers the qualitative event-based expert supervision approach to distillation column problems. The development of a general supervision in this work is based on an events generator and a corrective actions generator. The qualitative symbols are based on fuzzy sets. In particular, there are mechanisms for processing the changes in the system variables from qualitative symbols.     

一种面向对象的专家系统设计方法

近年来,已经有不少面向对象技术运用于专家系统的研究。本文提出一种新的面向对象专家系统的设计方法。首先介绍系统总体结构,给出知识表示模型和推理机制,并且引入RMG的概念,进一步提出运用RMG完成推理过程的算法。最后把提出的方法应用于中西医结合糖尿病诊疗专家系统的开发。实践证明此方法是有效的。     

故障诊断专家系统研究的现状与展望

介绍了专家系统在故障诊断领域的应用情况,阐述了国内外故障诊断专家系统的发展现状,针对目前研究较成熟的故障诊断专家系统模型遄行了分析,并指出了各自技术的特点和局限性;最后结合新技术的发展和应用对故障诊断专家系统的发展趋势进行了展望。     

模糊专家理论在炮兵指挥控制系统中的运用

针对炮兵指挥控制系统在作战训练中遇到的问题,在科学地分析了影响效果和相关因素的基础上,依据模糊专家理论的方法和原理,设计了相关的运用决策框架,并提出了运用规则。     

基于神经网络的专家系统控制器

采用平衡的倒摆小车所记录下来的数据，经处理后用有师学习方法来训练前馈神经网络。训练后的神经网络可以替换专家，并且表示了一组专家尚未意识到或者表达不出来的规则。用神经网络表示的规则与使用Ｑｕｉｎｌａｎ的ＩＤ３推理算法推导出的规则进行了比较。实验结果表明，神经网络算法学习出来的规则较ＩＤ３算法推导出的规则更为有效，且更有应用价值。本文最后尝试将该规则应用于火箭的姿态控制。     

基于神经网络和专家系统的铸造质量控制方法

提出了一种基于神经网络与专家系统,具有一定自学习能力的铸造质量控制方法,具体描述了其工作原理.该系统由工艺参数优化模块和缺陷诊断模块组成.工艺参数优化模块以神经网络为推理机,以过去生产数据和有限元数值模拟数据作为训练样本,建立工艺参数与铸件性能之间的非线性关系.缺陷诊断模块以基于产生式规则的推理方式,诊断缺陷类型、产生原因及防治措施,且诊断结果反馈到工艺参数优化模块,用于神经网络再学习.试验结果表明,该系统提高了工艺参数准确率,增强了缺陷诊断能力,减少了铸件次品率.     

一种专家地图构建方法

专家地图是专家个人知识和技能信息及专家协作网络信息的记录。基于图论的方法给出专家发现社会协作网的算法。定义一种从企业局域网的异构数据文档中发现专家地图的任务,给出实现该任务的多种模型,并将模型应用于TRCKMS系统的开发中。在W3C语料库上进行的实验表明,该模型能够提高专家发现的效率。     

基于波谱库的特色农作物智能专家分类系统设计

农作物的长势监测和产量估算一直是遥感技术应用的重要方面,而一个好的农作物分类算法对于农作物产量和长势进行监测十分关键。目前对于一些特色农作物而言,这方面的研究比较缺乏。因此拳研究设计了符合特色农作物的长势监测和产量测算功能模块,将数据挖掘和知识发现应用到专家分类算法中,自行开发了适合农作物数据发现和挖掘的归纳学习算法,充分利用了波谱库中大量的波谱数据、相关属性和空间数据,形成了基于波谱库的特色农作物智能专家分类系统。     

一种专家地图构建方法

专家地图是专家个人知识和技能信息及专家协作网络信息的记录。基于图论的方法给出专家发现社会协作网的算法。定义一种从企业局域网的异构数据文档中发现专家地图的任务,给出实现该任务的多种模型,并将模型应用于TRCKMS系统的开发中。在W3C语料库上进行的实验表明,该模型能够提高专家发现的效率。     

在专家系统中利用关系数据库来表达知识

在专家系统中,传统的知识表达方式具有很大的局限性,知识的重复利用率不高,并且无法给出知识的关系。为了解决这个问题,文章利用关系数据库的特点,将知识存贮在数据库中,并结合推理机的工作原理,阐明了如何利用标准数据查询语言(SQL)来对知识库进行正向推理及反向推理。从而提高了推理机的效率,减少知识获取的开销,增强知识库的可维护性和扩展性。