无损检测中基于模糊逻辑的多传感器数据融合

模糊逻辑在多传感器数据融合中得到广泛的应用,但是基于模糊逻辑的多传感器数据融合在无损检测中的应用还是一个崭新的课题,本文给出了一种新的用于无损检测数据融合的模糊逻辑结构,并且给出了隶度函数以及运算符的定义,最后给出了例子阐明了这种结构在无损检测中的应用。     

基于模糊逻辑的多传感器数据融合方法研究

针时当前工业环境下温湿度采集和多传感器数据融合的特点和需求,文章提出了一种基于模糊逻辑的多传感器数据融合方法,该方法能够消除错误数据对判别当前环境下温湿度状态的影响.文章首先介绍了温湿度检测系统的硬件构成,然后以模糊集理论为基础,研究了传感器数据的模糊转换,最后给出了基于模糊综合评判的多传感器数据融合方法.试验结果表明,本系统具有很强的实用性和智能性.     

基于模糊测度的模糊分支时态逻辑模型检测

针对具有模糊性和不确定性的复杂系统的验证问题,提出一种基于模糊测度的模糊分支时态逻辑模型检测算法。首先,在模糊决策过程模型的基础上引入模糊分支时态逻辑的语法和语义。然后,给出模糊分支时态逻辑模型检测算法,该算法将模型检测问题转化为矩阵运算,具有计算方式简洁、复杂度较低的优点。最后,通过医疗专家系统的实例说明了该模型检测算法的有效性。     

基于SelQPSO的WMR区间二型模糊逻辑控制

针对轮式移动机器人(WMR)的轨迹跟踪问题,首先根据WMR非线性模型设计了区间二型模糊逻辑控制器(Ⅱ2FLC);其次针对IT2FLC模糊规则中隶属函数参数难以确定问题,通过改进的量子粒子群算法(SelQPSO)优化IT2FLC的隶属函数参数.最后,将经过SelQPSO优化的IT2FLC控制效果分别与经过量子粒子群算法(QPSO)优化的IT2FLC、未经优化的IT2FLC以及T1FLC算法进行对比.此外,进一步考虑外部扰动分别对四种控制方法控制效果的影响.仿真结果表明,与另外三种控制方法相比,经过SelQPSO优化的IT2FLC具有更好的控制效果和抗干扰能力.     

基于模糊决策过程的模糊计算树逻辑模型检测

针对由数据表述产生的不确定性模糊系统的模型检测问题,给出模糊计算树逻辑模型检测算法。首先,引入模糊决策过程作为此类系统的模型,其最大特点是在迁移过程中对动作的不确定性选择和状态表述的模糊性。然后,在模糊决策过程基础上,给出模糊计算树逻辑的语法和语义。最后,给出模糊计算树逻辑模型检测算法,该算法是将模糊计算树逻辑模型检测问题转换为模糊矩阵的合成运算,其优势是时间复杂度低、计算过程较为简洁。     

一种基于模糊逻辑的实用睡意检测方法

提出一种基于普通摄像头快速有效的人眼定位和睡意检测方法,对光照、姿势和背景都具有较好的鲁棒性.首先从USB摄像头截取头肩部图像,经过光照补偿、运动检测和肤色处理粗定位人脸区域,再根据眼睛亮度、色度信息,数学形态学处理将眼部特征最大化,最后使用模糊逻辑筛选候选眼睛块,Hough圆检测判定眼睛状态.     

基于数值型数据的模糊规则快速挖掘方法

针对数值型数据能准确反应现实世界,但难以理解的问题,为了从数值型数据中挖掘出易于理解的知识,提出了基于数值型数据的模糊规则快速挖掘方法。该方法能从数值型数据中挖掘出一个零阶的Sugeno模糊规则,并采用一种启发式方法将这个零阶的Sugeno模糊规则的数值结论转变为两个带置信度的语言变量,并给出了规则库的存储结构。最后通过实例证明了这种快速模糊规则挖掘方法能避免复杂的数值型计算和能有效逼近非线性函数的优点．     

模糊交互时态逻辑的模型检测

交互时态逻辑已被广泛应用于开放系统的规范描述,交互时态逻辑的模型检测技术是一个比较重要的验证方法。为了形式化描述和验证具有模糊不确定性信息的开放系统的性质,提出了一种模糊交互时态逻辑,并讨论了它的模型检测问题。首先,引入了模糊交互时态逻辑的基于路径和基于不动点的两种语义,证明了其等价性。然后,基于其等价性,给出了模糊交互时态逻辑的模型检测算法和复杂性分析。     

一种基于类的Java多线程程序数据竞争静态检测算法

多线程并发程序的广泛使用引发了更多的数据竞争问题,竞争检测对于提高软件质量具有重要意义。将竞争静态检测和静态切片分析结合起来,提出了一种基于类的Java数据竞争静态检测算法,该算法利用函数调用层次获得函数调用链,对类域进行分析,找出可能数据竞争,通过静态切片缩小程序分析范围,并结合数据竞争的必要条件,去掉不可能数据竞争。实例表明,该算法可用于指导修复程序中的竞争缺陷。     

基于FPSO的电力巡检机器人的广义二型模糊逻辑控制

针对电力巡检机器人(Power-line inspection robot, PLIR)的平衡调节问题, 设计了广义二型模糊逻辑控制器(General type-2 fuzzy logic controller, GT2FLC); 针对GT2FLC中隶属函数参数难以确定的问题, 通过模糊粒子群(Fuzzy particle swarm optimization, FPSO)算法来优化隶属函数参数. 将GT2FLC的控制性能与区间二型模糊逻辑控制器(Interval type-2 fuzzy logic controller, IT2FLC)和一型模糊逻辑控制器(Type-1 fuzzy logic controller, T1FLC) 的控制性能进行对比. 除此之外, 还考虑了外部干扰对三种控制器控制效果的影响. 仿真结果表明, GT2FLC具有更好的性能和处理不确定性的能力.     

Type-2 fuzzy Gaussian mixture models

This paper presents a new extension of Gaussian mixture models (GMMs) based on type-2 fuzzy sets (T2 FSs) referred to as T2 FGMMs. The estimated parameters of the GMM may not accurately reflect the underlying distributions of the observations because of insufficient and noisy data in real-world problems. By three-dimensional membership functions of T2 FSs, T2 FGMMs use footprint of uncertainty (FOU) as well as interval secondary membership functions to handle GMMs uncertain mean vector or uncertain covariance matrix, and thus GMMs parameters vary anywhere in an interval with uniform possibilities. As a result, the likelihood of the T2 FGMM becomes an interval rather than a precise real number to account for GMMs uncertainty. These interval likelihoods are then processed by the generalized linear model (GLM) for classification decision-making. In this paper we focus on the role of the FOU in pattern classification. Multi-category classification on different data sets from UCI repository shows that T2 FGMMs are consistently as good as or better than GMMs in case of insufficient training data, and are also insensitive to different areas of the FOU. Based on T2 FGMMs, we extend hidden Markov models (HMMs) to type-2 fuzzy HMMs (T2 FHMMs). Phoneme classification in the babble noise shows that T2 FHMMs outperform classical HMMs in terms of the robustness and classification rate. We also find that the larger area of the FOU in T2 FHMMs with uncertain mean vectors performs better in classification when the signal-to-noise ratio is lower.     

A program-based anomaly intrusion detection scheme using multiple detection engines and fuzzy inference

In this paper, a hybrid anomaly intrusion detection scheme using program system calls is proposed. In this scheme, a hidden Markov model (HMM) detection engine and a normal database detection engine have been combined to utilise their respective advantages. A fuzzy-based inference mechanism is used to infer a soft boundary between anomalous and normal behaviour, which is otherwise very difficult to determine when they overlap or are very close. To address the challenging issue of high cost in HMM training, an incremental HMM training with optimal initialization of HMM parameters is suggested. Experimental results show that the proposed fuzzy-based detection scheme can reduce false positive alarms by 48%, compared to the single normal database detection scheme. Our HMM incremental training with the optimal initialization produced a significant improvement in terms of training time and storage as well. The HMM training time was reduced by four times and the memory requirement was also reduced significantly.     

Systematic design of a stable type-2 fuzzy logic controller

Stability is one of the more important aspects in the traditional knowledge of automatic control. Type-2 fuzzy logic is an emerging and promising area for achieving intelligent control (in this case, fuzzy control). In this work we use the fuzzy Lyapunov synthesis as proposed by Margaliot and Langholz [M. Margaliot, G. Langholz, New Approaches to Fuzzy Modeling and Control: Design and Analysis, World Scientific, Singapore, 2000] to build a Lyapunov stable type-1 fuzzy logic control system, and then we make an extension from a type-1 to a type-2 fuzzy logic control system, ensuring the stability on the control system and proving the robustness of the corresponding fuzzy controller.     

A type-2 fuzzy embedded agent to realise ambient intelligence in ubiquitous computing environments

In this paper, we present a novel approach for realising the vision of ambient intelligence in ubiquitous computing environments (UCEs). This approach is based on embedding intelligent agents in UCEs. These agents use type-2 fuzzy systems which are able to handle the different sources of uncertainty and imprecision in UCEs to give a good response. We have developed a novel system for learning and adapting the type-2 fuzzy agents so that they can realise the vision of ambient intelligence by providing a seamless, unobtrusive, adaptive and responsive intelligence in the environment that supports the activities of the user. The user’s behaviours and preferences for controlling the UCE are learnt online in a non-intrusive and life long learning mode so as to control the UCE on the user’s behalf. We have performed unique experiments in which the type-2 intelligent agent has learnt and adapted online to the user’s behaviour during a stay of five days in the intelligent Dormitory (iDorm) which is a real UCE test bed. We will show how our type-2 agents can deal with the uncertainty and imprecision present in UCEs to give a very good response that outperforms the type-1 fuzzy agents while using smaller rule bases.     

Polynomial regression interval-valued fuzzy systems

In recent years, the type-2 fuzzy sets theory has been used to model and minimize the effects of uncertainties in rule-base fuzzy logic system (FLS). In order to make the type-2 FLS reasonable and reliable, a new simple and novel statistical method to decide interval-valued fuzzy membership functions and probability type reduce reasoning method for the interval-valued FLS are developed. We have implemented the proposed non-linear (polynomial regression) statistical interval-valued type-2 FLS to perform smart washing machine control. The results show that our quadratic statistical method is more robust to design a reliable type-2 FLS and also can be extend to polynomial model.     

基于隐马尔可夫模型的网络入侵检测方法

介绍了基于隐马尔可夫模型的网络入侵检测系统的检测方法,并且建立了两个隐马尔可夫模型,通过对数据包的分析,得出系统的检测结果.实验数据表明,该方法能有效地提高异常检测效率,对入侵检测具有重要价值.     

A review on the applications of type-2 fuzzy logic in classification and pattern recognition

In this paper a review of type-2 fuzzy logic applications in pattern recognition, classification and clustering problems is presented. Recently, type-2 fuzzy logic has gained popularity in a wide range of applications due to its ability to handle higher degrees of uncertainty. In particular, there have been recent applications of type-2 fuzzy logic in the fields of pattern recognition, classification and clustering, where it has helped improving results over type-1 fuzzy logic. In this paper a concise and representative review of the most successful applications of type-2 fuzzy logic in these fields is presented.