具有自适应类警戒参数的模糊ARTMAP神经网络

提出了一种具有自适应类警戒参数的模糊ＡＲＴＭＡＰ神经网络,为不同的模糊ＡＲＴ的类族设置了不同的警戒测试参数,并在学习过程中进行适应调整。还提出了新的非交叠超方形以及非交叠的Ｎｅｓｔｅｄ超主形的建立与扩展学习规则。     

规则抽取的Fuzzy ARTMAP方法

着重阐述了如何使用有教师监督的自组织神经网络－模糊自适应共振映射网络（Ｆｕｚｚｙ ＡＲＴＭＡＰ）从例子中抽取知识规则。叙述了规则抽取中的两个细节：网络修剪，即删除那些对网络抽取规则贡献不大的节点及其相连的权值；权值的量化，以使系统最终能释译成一套可使用的规则。本文对Ｆｕｚｚｙ ＡＲＴＭＡＰ网络作了改进和简化，并用于医学上心电图（ＥＣＧ）信号中室性早搏（ＰＶＣ）诊断规则的自动获取，取得了比较满意的结     

一种改进的ART1算法及其在人像识别中的应用

本文通过对自适应共振理论（ＡＲＴ）ＡＲＴ１的研究,提出了一种改进的ＡＲＴ１算法,这种算法不仅具有ＡＲＴ１算法的所有优点,而且降低了ＡＲＴ１算法的识别识误差。该算法在人像识别中取得了令人满意的效果。     

神经网络ART模型在故障诊断中的应用

讨论了目前最成功的一种无导师神经网络模型──自适应谐振理论ＡＲＴ。分析了ＡＲＴ的工作原理，给出了ＡＲＴ的具体算法（已在ＰＣ－４８６上用Ｃ语言实现）；指出了ＡＲＴ的实质，并以“有轨自动物料搬运小车系统”为例详述了ＡＲＴ在故障诊断中的工作过程，获得了很好的结果。     

增量式IHMCAP算法的研究及其应用

增量式ＩＨＭＣＡＰ算法采用适用于混合型学习的ＦＴＡＲＴ神经网络，成功解决了符号学习与神经网络学习精度之间的均衡性问题。该算法还具有较强的增量学习能力，在给系统增加新的示例时，不用重新生成已有判定树和神经网络，只需进行一遍增量学习即可调整原结构以提高学习精度，效率高，速度快。     

采用模糊ARTMAP神经网络的字符识别方法

本文研究了将模糊ＡＲＴＭＡＰ神经网用于字符识别方法。实验证明这种将模糊技术与神经网络相结合的混合系统具有较高的识别率和较快的识别速度，采用ＡＲＴＭＡＰ神经网络只需要较少的网络训练时间。     

一种用神经技术学习模糊分类规则的方法

本文提出一种用神经网络技术学习模糊分类规则的算法－有导师共振竞争学习算法（ＳＲＣＬ）。ＳＲＣＬ方法有机地把无导师ＡＲＴ学习方法和有导师竞争学习方法结合起来，可有效地学习模糊分类规则。警戒线参数是自适应变化的，从而可自动地确定连结权向量的个数。言语中给出一个数字例了，并对实验结果进行了分析。     

混合型多概念获取系统的设计与实现

本文主要描述了一个增量式混合型多概念获取系统ＨＭＣＡＳ，它提出了一个基于概率论的符号学习与神经网络学习相结合的学习算法，能从隶属于某个概念集的实例集中归纳出满足用户精度要求的，以浊合型判定树表示的概念描述。在ＨＭＣＡＳ中，符号学习与神经网络学习具有结合紧密的转换灵活等特点，具有较高的学习效率和较强的归纳能力以及增量学习能力。ＨＭＣＡＳ的神经网络学习可选择ＢＰ网络或ＦＴＡＲＴ网络，其推理机制提供了混     

基于人工神经网络的FMS物料搬运机器人的故障诊断

本文讨论了一种无导师的神经模型－－自适应谐振理论ＡＲＴ，详细分析了ＡＲＴ的工作原理及故障诊断过程，本文以ＦＭＳ中的物料搬运机器人的故障诊断为例，详细说明了故障样本编码，ＡＲＴ的自学习，智能化诊断过程，并给出了仿真结果（在ＰＣ－４８６／３３上实现），仿真结果表明ＡＲＴ是一种有效且实用的故障诊断方法。     

FTART2神经网络及其规则抽取研究

本文提出了一种基于域理论的自适应谐振神经网络算法ＦＴＡＲＴ２,算法将自适应谐振理论和域理论的优点有要结合,不需人为设置隐层神经元,学习速度快,精度高。此外,本文不提出了一种从ＦＴＡＲＴ２网络中抽取符号规则的方法。实验结果表明,使用该方法抽取出的符号规则可理解性好,预测精度高,可以很好地描述了ＦＴＡＲＴ２网络的性能。     

Classification on defective items using unidentified samples

A set of unlabelled items is used to establish a decision rule to classify defective items. The lifetime of an item has an exponential distribution. It is known that the Bayes decision rule, which classifies good and defective items, gives a minimum probability of misclassification. The Bayes decision rule needs to know the prior probability (defective percentage) and two mean lifetimes. In the set of unidentified samples, the defective percentage and two mean lifetimes are unknown. Hence, before we can use the Bayes decision rule, we have to estimate the three unknown parameters. In this study, a set of unlabelled samples is used to estimate the three unknown parameters. The Bayes decision rule with these estimated parameters is an empirical Bayes (EB) decision rule. A stochastic approximation procedure using the set of unidentified samples is established to estimate the three unknown parameters. When the size of unlabelled items increases, the estimates computed by the procedure converge to the real parameters and hence gradually adapt our EB decision rule to be a better classifier until it becomes the Bayes decision rule. The results of a Monte Carlo simulation study are presented to demonstrate the convergence of the correct classification rates made by the EB decision rule to the highest correct classification rates given by the Bayes decision rule.     

决策表的一种知识约简与规则获取方法

粗糙集理论是一种新型的数据挖掘和决策分析方法,利用粗糙集理论进行决策表的知识约简与决策规则挖掘已经成为研究热点。文中介绍了粗糙集的基本理论,在此基础上运用该理论对从决策表中获取最小规则进行了研究,提出了决策表约简的启发式方法,并通过一个具体实例详细说明了决策规则获取过程,实例分析表明了其有效性。     

一种基于局部属性集的规则确定度

在传统的粗糙集中,利用确定度来评测决策规则的确定性,然而当多个决策规则拥有相同的确定度时,对给定的对象进行分类变得困难。基于局部属性集对决策规则确定性的影响,提出一种新的规则确定度,能充分反映决策规则的确定性在局部属性集上的差异。实例表明新的确定度对决策规则有较好的评测。     

决策信息系统中挖掘全部决策规则的算法

在粗糙集理论的基础上,对决策信息系统中边界区域的数据进行研究,提出一种从边界区域数据中挖掘决策规则的算法——近似序列决策规则挖掘算法。在16个UCI数据集上的测试表明,该算法在规则的准确度和平均前件长度2个指标上优于ID3算法,能简洁、高效地挖掘出决策信息系统中的全部决策规则,为挖掘未知知识提供了新的思路。针对挖掘出的全部决策规则,提出新的确定性度量和一致性度量指标,用以准确地反映决策规则的性能。     

Recursive implementation of a two-step nonparametric decision rule

The two-step approach to nonparametric discrimination is that of estimating class-conditional densities and deriving the Bayes decision rule as if the estimates were true. Direct implementation of such a decision rule ecounters two computational problems. Complexity increases with sample size, and finite precision limits the decision rule domain. Here a recursive algorithm to reduce the expected number of operations and word-length limitations below that of the direct approach is developed. A special case of the formulation reduces to the weighted k-nearest-neighbor rule.     

Vague集的综合决策规则在方案优选中的应用

总结出一种Vague集的综合决策规则,它是Vague集的模式识别方法的一个特例。给出该综合决策规则在方案优选中的一个应用实例。     

Adaptive management of natural systems using fuzzy logic

Hypotheses about how management practices influence ecosystem services can be tested using a crisp, probability-based, or fuzzy decision rule. The correct decision rule depends on whether: (1) the observed state of an ecosystem service (x) is non-stochastic or stochastic; (2) the true state of the ecosystem service (y) is non-stochastic or stochastic; and (3) the relationship between x and y is deterministic, stochastic, or uncertain. Crisp and probability-based decision rules are not appropriate when the relationship between y and x is uncertain in the sense that the decision maker is unable or unwilling to specify conditional probabilities of y given x. Under these conditions, a fuzzy decision rule is appropriate. A hypothetical case study is used to illustrate how a fuzzy decision rule is used to test hypotheses about whether selective cutting of timber provides greater or less forest biodiversity than clearcutting of timber. The case study describes how to incorporate the decision rule in an active adaptive management framework to sequentially test the extent to which changes over time in other factors influencing ecosystem services, such as greater spread of invasive species due to global warming, alter the efficacy of timber management practices. The fuzzy adaptive management decision rule can be generalized to account for the effects of management practices on multiple ecosystem services.     

Rule sets based bilevel decision model and algorithm

Bilevel decision addresses the problem in which two levels of decision makers, each tries to optimize their individual objectives under certain constraints, act and react in an uncooperative, sequential manner. As bilevel decision making often involves many uncertain factors in real world problems, it is hard to formulate the objective functions and constraints of the leader and the follower in modelling a real bilevel decision problem. This study explores a new approach that uses rule sets to formulate a bilevel decision problem. It first develops related theories to prove the feasibility to model a bilevel decision problem by rule sets. It then proposes an algorithm to describe the modelling process. A case study is discussed to illustrate the functions and effectiveness of the proposed rule sets based bilevel decision modelling algorithm.     

Robust Cluster Analysis of Discrete Multivariate Observations

Cluster analysis of discrete multivariate observations containing outliers in a sample of a mixture of polynomial distributions is studied. A robust decision rule (stable to outliers) is designed in terms of analytically computed risk (probability of erroneous classification). A practical method of realization of the decision rule as a robust clustering procedure is developed and its effectiveness is determined both analytically and experimentally.