一种新的三角模糊数算子在加权模糊推理中的应用

针对基于模糊逻辑的加权模糊推理,Chen Shy i-M ing提出了两种计算合取式前件整体真值的方法。由于所用模糊数算子的影响,两种方法的求取结果在准确性和合理性上都存在一定的缺陷。这种缺陷将直接影响推理的性能。因此,为改善这种缺陷,提高推理性能,本文提出了一种新的三角模糊数算子。它的应用可以提高推理的准确性和合理性。     

模糊时间序列模型研究综述

模糊时间序列模型是为解决经典时间序列分析方法不能处理模糊问题而诞生的,随着人们解决复杂问题需要的日益增加,对它的研究越来越深入,应用越来越广泛。首先,在简单介绍模糊时间序列模型框架的基础上,本文总结了预测模型建立过程中的几个关键问题,分析了现有研究成果就这些问题的处理方法的优点与不足;随后介绍了模型的应用现状;最后,就模型的研究趋势及相关问题探讨了模糊时间序列模型的研究方向。     

模糊时间序列模型的辨识

可能性线性系统的输出时间序列可用模糊数来表示，我们称其为模糊时间序列（ＦＴＳ），这篇论文提出了ＦＴＳ分析的新方法，并研究它的参数估计和模型定价。两个仿真例子表明本文提出的方法对ＦＴＳ分析是非常有效的。     

大窑湾入境集装箱量及疫情集装箱量的模糊预测研究

作为国际海运和物流业的主要装载工具集装箱是入境统计以及检验检疫的主要对象.按特定规律统计的集装箱量时间序列既有一定的统计规律,又有较大的随机性.采用模糊时间序列方法,通过构造论域、模糊集以及提出多重计算规则,对2005年6月至2012年10月大窑湾入境集装箱量、疫情集装箱量,以及疫情与入境集装箱量比值时间序列进行了模糊分析与预测.预测值与实际值的比较说明了算法的有效性.预测值与实际值的平均绝对误差、平均绝对百分误差从整体上揭示了时间序列自身的模糊性和模型的合理性.     

模糊网络关键路径问题的模糊线性规划方法

构建了模糊线性规划模型及其对偶规划模型,用于求解工期模糊情况下的项目关键路径问题,克服了传统的正向和逆向递推方法中存在的计算繁琐问题.在模糊线性规划模型中,通过枚举不同α—cut值,利用一种基于区间数距离测度的模糊数排序法,改进了现有模糊线性规划模型目标函数,计算出模糊总工期和所有可能的关键路径,解决了现有模糊线性规划模型构建中,未能同时考虑到工期模糊时关键路径可能会发生改变问题以及项目可能存在多条关键路径问题.在对偶规划模型中,通过对项目活动模糊时间参数基于α—cut重新定义,求解出活动的模糊时间参数,克服了已有模糊线性规划模型中要么仅能求出事项(节点)的模糊时间参数但未求出活动的模糊时间参数,要么求出了活动模糊时间参数但其最晚完成时间参数定义不正确的缺陷.     

时间序列中条件异方差性的检验

对时间序列中条件异方差性的检验 ,提出了一种新的方法 .这种检验方法的构造是基于拟合优度类检验统计量和Cramer vonMises类检验统计量 .研究了检验统计量的渐近性质 .结果表明 ,新的检验是相合的 .     

Fuzzy命题逻辑中的Fuzzy推理

在近似推理的关系合成规则中,用于表现蕴涵命题的Fuzzy关系依赖于前、后件的具体意义。为了克服这个缺点,Baldwin提出了真值限定方法。其后,他在[2]中又将这种方法推广到不限于蕴涵命题的更一般情形。本文的目的是发展[2]的工作,系统地讨论Fuzzy命题逻辑中的Fuzzy推理的性质。     

钢厂生产的模糊作业时间及其在管理中的应用

结合抚顺钢厂的实际,利用模糊数学理论对生产实际中的模糊作业时间进行处理,将模糊作业时间转换为非模糊精确问题,利用禁忌搜索智能优化方法对问题进行求解,使调度计划具有一定的柔性,让管理者能够掌握调度时间范围,从而使计划调度更接近现实,大大缩短了总完工时间,对于钢厂一体化管理,使连铸生产的高温铸坯能够在允许时间范围内到达热轧厂,降低了能源消耗,缩短了生产周期。     

一类不分明时间序列的回归预测

研究了一类不分明时间序列的线性回归预测问题,通过模糊数空间中的距离,建立了模糊环境中最小二乘回归模型,证明了回归模型解的存在性和唯一性,并给出了确定模型的模糊参数及检验模型拟合度的计算公式。     

模糊逼近集与模糊逼近泛函映射

本文通过对模糊逼近集与模糊逼近泛函映射的建立和研究，为时间序列预测工作开辟新的途径，建立新的方法·     

Learning temporal nodes Bayesian networks

Temporal Nodes Bayesian Networks (TNBNs) are an alternative to Dynamic Bayesian Networks for temporal reasoning with much simpler and efficient models in some domains. TNBNs are composed of temporal nodes, temporal intervals, and probabilistic dependencies. However, methods for learning this type of models from data have not yet been developed. In this paper, we propose a learning algorithm to obtain the structure and temporal intervals for TNBNs from data. The method consists of three phases: (i) obtain an initial approximation of the intervals, (ii) obtain a structure using a standard algorithm and (iii) refine the intervals for each temporal node based on a clustering algorithm. We evaluated the method with synthetic data from three different TNBNs of different sizes. Our method obtains the best score using a combined measure of interval quality and prediction accuracy, and a competitive structural quality with lower running times, compared to other related algorithms. We also present a real world application of the algorithm with data obtained from a combined cycle power plant in order to diagnose temporal faults.     

Knowledge Representation, Reasoning and Integration Using Temporal Logic with Clocks

Representation, reasoning about and integrating knowledge based on multiple time granularities in knowledge-based systems is important, especially when talking about events that take place in the real world. Formal approaches based on temporal logics have been successfully applied in many application domains of knowledge-based systems where the evolution of a system and its environment through time is central. This paper presents a methodology based on temporal logic to deal with knowledge based on multiple time granularities in knowledge-based systems. The temporal logic we consider is especially suitable for modelling events with different rates and/or scales of progress. The methodology includes an approach to the representation of timing systems, a method used for representing facts and rules in a knowledge-based system that involve multiple time granularities using temporal logic, and several deductive reasoning techniques. The work presented in this article has been supported in part by The Australian Research Council and Macquarie University. Note that this paper is an extended and revised version of Orgun, Liu and Nayak [37].     

Nonlinear scheme with high accuracy for nonlinear coupled parabolic-hyperbolic system

A nonlinear finite difference scheme with high accuracy is studied for a class of two-dimensional nonlinear coupled parabolic-hyperbolic system. Rigorous theoretical analysis is made for the stability and convergence properties of the scheme, which shows it is unconditionally stable and convergent with second order rate for both spatial and temporal variables. In the argument of theoretical results, difficulties arising from the nonlinearity and coupling between parabolic and hyperbolic equations are overcome, by an ingenious use of the method of energy estimation and inductive hypothesis reasoning. The reasoning method here differs from those used for linear implicit schemes, and can be widely applied to the studies of stability and convergence for a variety of nonlinear schemes for nonlinear PDE problems. Numerical tests verify the results of the theoretical analysis. Particularly it is shown that the scheme is more accurate and faster than a previous two-level nonlinear scheme with first order temporal accuracy.     

作用模糊子集推理方法的研究与应用

针对实用模糊控制过程,提出作用模糊子集和作用模糊控制规则的概念;根据模糊逻辑推理中真值的产生、传递和接收机理,提出作用模糊子集推理方法;比较分析了作用模糊子集推理方法与CRI法的推理结果;利用该推理方法实现了试验室温度模糊控制试验。     

A compensation-based recurrent fuzzy neural network for dynamic system identification

In this paper, a type of compensation-based recurrent fuzzy neural network (CRFNN) for identifying dynamic systems is proposed. The proposed CRFNN uses a compensation-based fuzzy reasoning method, and has feedback connections added in the rule layer of the CRFNN. The compensation-based fuzzy reasoning method can make the fuzzy logic system more adaptive and effective, and the additional feedback connections can solve temporal problems. The CRFNN model is proven to be a universal approximator in this paper. Moreover, an online learning algorithm is proposed to automatically construct the CRFNN. The results from simulations of identifying dynamic systems have shown that the convergence speed of the proposed method is faster than the convergence speed of conventional methods and that only a small number of tuning parameters are required.     

Reasoning with contextual graphs

Decision trees allow the modeling of event-dependent reasoning, but do not consider the dynamics of contextual changes in reasoning. In the framework of the SART project, which aims at the design and development of an intelligent support system for subway regulators, we have to model highly contextual reasoning. We introduce the notion of contextual graph to take into account temporal and context-based reasoning. This model relies on observed reasoning modes in which the context and its dynamics are essential.     

Computational complexity of hybrid interval temporal logics

Interval logics are very expressive temporal formalisms, but reasoning with them is often undecidable or has high computational complexity. As a result, a vast number of approaches limiting their expressive power—in order to obtain better computational behaviour—have been introduced. Unfortunately, due to such restrictions, interval logics often lose referentiality, that is, the capacity to refer to specific time intervals, which is crucial for temporal representation and reasoning. The computational price that needs to be paid in order to regain referentiality is not well studied and our research aims to fill this gap. In particular we study the main interval temporal logic, called the Halpern-Shoham logic, and its low complexity modifications. To regain referentiality in these modifications, we extend the language with the hybrid machinery—nominals and satisfaction operators—and classify the obtained logics according to their computational complexity. We show that such a hybridisation often makes tractable logics intractable but not undecidable. This allows us to construct hybrid interval temporal logics which are referential as well as maintain a good compromise between expressiveness and complexity; it makes them valuable formalisms for temporal knowledge representation. We also introduce a class of models which, due to a specific interplay between the interpretation of modal operators and a structure of time, makes reasoning in interval logics computationally hard even in the absence of the hybrid machinery.     

An efficient nonlinear iteration scheme for nonlinear parabolic-hyperbolic system

A nonlinear iteration method named the Picard-Newton iteration is studied for a two-dimensional nonlinear coupled parabolic-hyperbolic system. It serves as an efficient method to solve a nonlinear discrete scheme with second spatial and temporal accuracy. The nonlinear iteration scheme is constructed with a linearization-discretization approach through discretizing the linearized systems of the original nonlinear partial differential equations. It can be viewed as an improved Picard iteration, and can accelerate convergence over the standard Picard iteration. Moreover, the discretization with second-order accuracy in both spatial and temporal variants is introduced to get the Picard-Newton iteration scheme. By using the energy estimate and inductive hypothesis reasoning, the difficulties arising from the nonlinearity and the coupling of different equation types are overcome. It follows that the rigorous theoretical analysis on the approximation of the solution of the Picard-Newton iteration scheme to the solution of the original continuous problem is obtained, which is different from the traditional error estimate that usually estimates the error between the solution of the nonlinear discrete scheme and the solution of the original problem. Moreover, such approximation is independent of the iteration number. Numerical experiments verify the theoretical result, and show that the Picard-Newton iteration scheme with second-order spatial and temporal accuracy is more accurate and efficient than that of first-order temporal accuracy.     

A generalized time quantifier approach to approximate reasoning

In this paper, a rather expressive fuzzy temporal logic for linear time is introduced. First, this logic is a multivalued generalization (Lukasiewicz style) of a two-valued linear-time temporal logic based on, e.g., the “until” operator. Second, it is obtained by introducing a generalized time quantifier (a generalization of the partition operator investigated by Shen) applied to fuzzy time sets.In this fuzzy temporal logic, generalized compositional rules of inference, suitable for approximate reasoning in a temporal setting, are presented as valid formulas.Some medical examples illustrate our approach.     

一种基于证据推理的信息不完全的多准则决策方法

针对权系数信息不完全、准则值不确定且不完全的多准则决策问题,提出了一种基于证据推理的方法.该方法通过证据推理算法构造方案的目标函数,结合不完全信息的权系数建立非线性规划模型,使用遗传算法求解模型得到效用值的区间数,从而得到整个方案集的排序.最后以实例表明该方法的有效性和可行性.