共查询到20条相似文献,搜索用时 15 毫秒
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利用动态在线调度方法对动态环境下的作业车间进行研究,采用优先级调度规则对大量调度案例进行求解,针对7个调度目标,从备选调度规则集中选出了单个目标下性能最优的调度规则;为实现调度规则的动态选择以适应多目标调度,基于免疫系统中的独特型网络理论,设计了一种免疫调度算法.根据算法,定义了有效的抗体和抗原结构,并通过抗体间亲和力计算、抗体浓度计算、抗体选择等关键步骤,实现对调度规则的动态控制.仿真测试数据表明,所设计的免疫调度算法能根据不同的车间情况,快速选出不同的调度规则满足多个调度目标,有效解决了作业车间多目标调度问题. 相似文献
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调度规则是解决实际生产中的动态车间作业调度问题的有效方法,但它一般只在特定调度环境下性能较好,当环境发生变化时,就需要进行实时选择和评价。对调度规则的实时选择和评价方法进行综述,以研究实际生产中动态车间的实时调度问题。对调度规则的发展、分类以及特点进行了概述,并对调度规则的选择和评价方法进行了总结。详细介绍了调度规则的选择方法,包括使用较多的稳态仿真方法和表现较好的人工智能方法,并给出了仿真方法、专家系统、机器学习方法以及人工神经网络方法,用于调度规则的选择时所取得的研究成果和结论。此外,还介绍了调度规则的评价指标及评价方法。最后针对调度规则存在的不足,指出了未来的研究方向。 相似文献
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This paper deals with learning first-order logic rules from data lacking an explicit classification predicate. Consequently, the learned rules are not restricted to predicate definitions as in supervised inductive logic programming. First-order logic offers the ability to deal with structured, multi-relational knowledge. Possible applications include first-order knowledge discovery, induction of integrity constraints in databases, multiple predicate learning, and learning mixed theories of predicate definitions and integrity constraints. One of the contributions of our work is a heuristic measure of confirmation, trading off novelty and satisfaction of the rule. The approach has been implemented in the Tertius system. The system performs an optimal best-first search, finding the k most confirmed hypotheses, and includes a non-redundant refinement operator to avoid duplicates in the search. Tertius can be adapted to many different domains by tuning its parameters, and it can deal either with individual-based representations by upgrading propositional representations to first-order, or with general logical rules. We describe a number of experiments demonstrating the feasibility and flexibility of our approach. 相似文献
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在数据库中发现具有时态约束的关联规则 总被引:50,自引:0,他引:50
目前,国际上的关联规则研究尚未考虑时间因素.然而,时间是现实世界的固有属性,许多现实 世界数据库都存在时态语义问题.该文考察称为有效时间的时态约束问题,提出了时间区间延 展与归并技术以及新的时态关联规则发现算法,从而进一步推广了关联规则的应用. 相似文献
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文章探讨了在目标信息系统中,如何利用fuzzy规则的知识发现来计算条件属性和目标属性之间fuzzy关系的包含程度,并将其应用在一大型乳业集团的CRM系统中来刻画客户等级评定指标和评定结果之间的包含度,采用ASP+XML和MSSQL Server2000数据库实现,为该集团评估客户的信用等级提供了科学的方法。 相似文献
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Genetic Programming-based Construction of Features for Machine Learning and Knowledge Discovery Tasks 总被引:1,自引:0,他引:1
Krzysztof Krawiec 《Genetic Programming and Evolvable Machines》2002,3(4):329-343
In this paper we use genetic programming for changing the representation of the input data for machine learners. In particular, the topic of interest here is feature construction in the learning-from-examples paradigm, where new features are built based on the original set of attributes. The paper first introduces the general framework for GP-based feature construction. Then, an extended approach is proposed where the useful components of representation (features) are preserved during an evolutionary run, as opposed to the standard approach where valuable features are often lost during search. Finally, we present and discuss the results of an extensive computational experiment carried out on several reference data sets. The outcomes show that classifiers induced using the representation enriched by the GP-constructed features provide better accuracy of classification on the test set. In particular, the extended approach proposed in the paper proved to be able to outperform the standard approach on some benchmark problems on a statistically significant level. 相似文献
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Mathieu Serrurier Henri Prade 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2007,11(5):459-466
Introducing fuzzy predicates in inductive logic programming may serve two different purposes: allowing for more adaptability
when learning classical rules or getting more expressivity by learning fuzzy rules. This latter concern is the topic of this
paper. Indeed, introducing fuzzy predicates in the antecedent and in the consequent of rules may convey different non-classical
meanings. The paper focuses on the learning of gradual and certainty rules, which have an increased expressive power and have
no simple crisp counterpart. The benefit and the application domain of each kind of rules are discussed. Appropriate confidence
degrees for each type of rules are introduced. These confidence degrees play a major role in the adaptation of the classical
FOIL inductive logic programming algorithm to the induction of fuzzy rules for guiding the learning process. The method is
illustrated on a benchmark example and a case-study database. 相似文献
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The problem of learning decision rules for sequential tasks is addressed, focusing on the problem of learning tactical decision rules from a simple flight simulator. The learning method relies on the notion of competition and employs genetic algorithms to search the space of decision policies. Several experiments are presented that address issues arising from differences between the simulation model on which learning occurs and the target environment on which the decision rules are ultimately tested. 相似文献
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Laura M?ru?ter A. J. M. M. Weijters Wil M. P. Van Der Aalst Antal Van Den Bosch 《Data mining and knowledge discovery》2006,13(1):67-87
Effective information systems require the existence of explicit process models. A completely specified process design needs
to be developed in order to enact a given business process. This development is time consuming and often subjective and incomplete.
We propose a method that constructs the process model from process log data, by determining the relations between process
tasks. To predict these relations, we employ machine learning technique to induce rule sets. These rule sets are induced from
simulated process log data generated by varying process characteristics such as noise and log size. Tests reveal that the
induced rule sets have a high predictive accuracy on new data. The effects of noise and imbalance of execution priorities
during the discovery of the relations between process tasks are also discussed. Knowing the causal, exclusive, and parallel
relations, a process model expressed in the Petri net formalism can be built. We illustrate our approach with real world data
in a case study.
相似文献
Antal Van Den BoschEmail: |
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本文通过对跗建模和公式发现进行分析,提出了基于遗传建模的公式发现算法,并给出了具体的描述,对算法中关键对象的数据结构给出了具体的设计,最后针对数列归纳和公式发现给出了运行结果。 相似文献
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Katharina Probst Lori Levin Erik Peterson Alon Lavie Jaime Carbonell 《Machine Translation》2002,17(4):245-270
The AVENUE project contains a run-time machine translationprogram that is surrounded by pre- and post-run-time modules. Thepost-run-time module selects among translation alternatives. Thepre-run-time modules are concerned with elicitation of data andautomatic learning of transfer rules in order to facilitate thedevelopment of machine translation between a language with extensiveresources for natural language processing and a language with fewresources for natural language processing. This paper describes therun-time transfer-based machine translation system as well as two ofthe pre-run-time modules: elicitation of data from the minoritylanguage and automated learning of transfer rules from theelicited data. 相似文献
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针对目前归纳逻辑程序设计(inductive logic programming,ILP)系统要求训练数据充分且无法利用无标记数据的不足,提出了一种利用无标记数据学习一阶规则的算法——关系tri-training(relational-tri-training,R-tri-training)算法。该算法将基于命题逻辑表示的半监督学习算法tri-training的思想引入到基于一阶逻辑表示的ILP系统,在ILP框架下研究如何利用无标记样例信息辅助分类器训练。R-tri-training算法首先根据标记数据和背景知识初始化三个不同的ILP系统,然后迭代地用无标记样例对三个分类器进行精化,即如果两个分类器对一个无标记样例的标记结果一致,则在一定条件下该样例将被标记给另一个分类器作为新的训练样例。标准数据集上实验结果表明:R-tri-training能有效地利用无标记数据提高学习性能,且R-tri-training算法性能优于GILP(genetic inductive logic programming)、NFOIL、KFOIL和ALEPH。 相似文献
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论文首先对一种基于关联规则分类的算法做出了分析。然后对算法中的类关联规则的提取方法进行了改进,得到了一种新的基于关联规则分类的算法。并结合棉花病虫害数据运行的结果对两种算法的运行效率和实用性进行了比较。 相似文献
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需求开发的质量对软件有着直接的影响,而需求优先级的设定又是需求开发的一个重要环节。由于商业成品软件(COTS)的自身特点,使得其难以直接复用其他软件产品领域需求开发的方法和过程。层次分析法(AHP)对于复杂问题的决策非常有效,但是独立的AHP模型很难全面反映问题的本质,对此文中提出了一个改进的AHP方法,将其运用于COTS产品需求优先级的确定中,最后通过实例阐明了该方法在COTS产品需求优先级合成过程中的作用。 相似文献
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一种基于粗糙-模糊集理论的分类规则挖掘方法 总被引:1,自引:0,他引:1
提出了一种基于粗糙-模糊集理论的分类规则挖掘方法,以解决信息不完整情况下的推理和决策问题,并给出了该方法的流程图。利用基于粗糙集的特征属性约简算法和基于模糊集的决策规则归纳方法,可以挖掘出样本中隐藏的关联规则,形成决策。最后,将其应用于一个具体的信息系统中,结果令人满意,证明该方法是可行的且是有效的。 相似文献