基于Nie-Tan算法的区间二型模糊集质心计算:改变主变量采样个数

计算区间二型模糊集的质心(也称降型)是区间二型模糊逻辑系统中的一个重要模块。Karnik-Mendel(KM)迭代算法通常被认为是计算区间二型模糊集质心的标准算法。尽管如此,KM算法涉及复杂的计算过程,不利于实时应用。在各种改进类算法中,非迭代的Nie-Tan(NT)算法可节省计算消耗。此外,连续版本NT(CNT,continuous version of NT)算法被证明是计算质心的准确算法。本文比较了离散版本NT算法中求和运算和连续版本NT算法中求积分运算,通过四个计算机仿真例子证实了当适度增加区间二型模糊集主变量采样个数时,NT算法的计算结果可以精确地逼近CNT算法。     

小直径棒材超声横波检测仿真分析

采用超声水浸聚焦斜入射方式进行小棒材表面、近表缺陷检测时,声波在水/棒和棒/缺陷界面会发生反射、折射、散射、衍射及波形转换等一系列物理变换。此时缺陷可检性对声波入射条件十分敏感,常出现由于检测条件设置不当而导致缺陷漏检、误检的情况。为解决这一问题,本文针对小棒材超声斜入射检测中的主要参数——入射角和水距,开展声场及缺陷声响应仿真,研究检测参数对不同部位缺陷检测能力的影响,并对仿真结果进行试验验证。通过研究得到了检测水距、入射角度对缺陷检测能力的影响,并得到最优检测条件。试验验证结果表明研究制定的检测方案可有效检测出表面、近表面裂纹缺陷。     

Stability analysis of fractional order fuzzy cellular neural networks with leakage delay and time varying delays

In this paper we investigated the stability of fractional order fuzzy cellular neural networks with leakage delay and time varying delays. Based on Lyapunov theory and applying bounded techniques of fractional calculation, sufficient criterion are established to guarantee the stability. Hybrid feedback control is applied to derive the proposed results. Finally, numerical examples with simulation results are given to illustrate the effectiveness of the proposed method.     

峨山褐煤的分子结构和分子模拟

作为重要的化石能源，褐煤资源潜力巨大、分布广泛但综合利用率低。研究褐煤的分子结构模型，有助于预测褐煤在热解、液化和气化过程中的化学反应机理及反应路径，进而提高褐煤的综合应用水平。以云南峨山褐煤为研究对象，利用傅里叶变换红外光谱、13C核磁共振波谱及X射线光电子能谱等分析测试方法，获取了峨山褐煤的含碳、含氧及含氮结构参数。在此基础上，借助Gaussian 09计算平台，采用量子化学建模的方法构建并优化了峨山褐煤的分子结构模型。研究结果表明：峨山褐煤的芳碳率为39.20%，芳香碳结构主要为苯和萘，且芳香桥头碳与周边碳的比值χ_b为0.07；脂碳率为49.51%，脂肪碳结构主要为亚甲基，季碳和氧接脂碳；氧原子主要存在于羟基、醚氧、羰基和羧基结构中；含氮结构则以吡啶为主。基于元素分析、13C 核磁共振波谱分析，又经过热重实验消除褐煤中残余水分的影响后，计算出峨山褐煤的分子式为C₁₅₃H₁₃₇O₃₅N₂。依据分子式及分析结果计算出峨山褐煤的结构单元含量并构建出其初始结构模型，采用半经验法PM 3基组及密度泛函理论M06-2X/3-21G基组对初始分子构型进行优化。优化后的分子模型具有明显的三维立体特征，芳香环之间较为分散且在空间中排列不规则，芳香簇主要通过亚甲基、醚氧基、羰基、酯基和脂肪环连接，含氧官能团主要分布在分子边缘，脂肪族侧链较多。对优化后的分子模型进行振动频率计算进而获得了分子模型的模拟红外光谱，其与实验红外谱图吻合度良好，证明了峨山褐煤分子结构模型的准确性、合理性。分子结构模型的构建有利于直观地了解峨山褐煤的分子结构特征，从而有助于从微观分子角度研究峨山褐煤的宏观性质。同时，峨山褐煤分子结构模型可为其在热解、液化和气化等领域研究中提供理论指导。     

An approach for solving a fuzzy multiobjective programming problem

In this paper we present a new approach, based on the Nearest Interval Approximation Operator, for dealing with a multiobjective programming problem with fuzzy-valued objective functions.     

Fuzzy logic modifications of the Analytic Hierarchy Process

The Analytic Hierarchy Process (AHP) is a measurement methodology based on pair-wise comparisons that relies on judgment to derive priority scales. During its implementation, one constructs hierarchies, then makes judgments or performs measurements on pairs of elements with respect to a criterion to derive preference scales, which are then synthesized throughout the structure to select the preferred alternative.One of the areas where the AHP finds application is in the subjective phases of risk assessment (RA), where it is used to structure and prioritize diverse risk factors, including the judgments of experts. Since fuzzy logic (FL) has been shown to be an effective tool for accommodating human experts and their communication of linguistic variables, there has been research aimed at modeling the fuzziness in the AHP (FAHP), and recently the focus of some of that modeling has been with respect to RA.The literature discusses more than one FAHP model, which raises the question as to which are the prominent models and what are their characteristics. In response to this question, we examine three of the most influential FAHP models. The article proceeds as follows. It begins with a brief overview of the AHP and its limitations when confronted with a fuzzy environment. This is followed by a discussion of FL modifications of the AHP. A RA-based likelihood score example is used throughout. The article ends with a commentary on the findings.     

The valuation of life contingencies: A symmetrical triangular fuzzy approximation

This paper extends the framework for the valuation of life insurance policies and annuities by Andrés-Sánchez and González-Vila (2012, 2014) in two ways. First we allow various uncertain magnitudes to be estimated by means of fuzzy numbers. This applies not only to interest rates but also to the amounts to be paid out by the insurance company. Second, the use of symmetrical triangular fuzzy numbers allows us to obtain expressions for the pricing of life contingencies and their variability that are closely linked to standard financial and actuarial mathematics. Moreover, they are relatively straightforward to compute and understand from a standard actuarial point of view.