Some picture fuzzy Bonferroni mean operators with their application to multicriteria decision making

In this paper, we extend the Bonferroni mean (BM) operator with the picture fuzzy numbers (PFNs) to propose novel picture fuzzy aggregation operators and demonstrate their application to multicriteria decision making (MCDM). On the basis of the algebraic operational rules of PFNs and BM, we introduce some aggregation operators: the picture fuzzy Bonferroni mean, the picture fuzzy normalized weighted Bonferroni mean, and the picture fuzzy ordered weighted Bonferroni mean. Then, a new picture fuzzy MCDM method is proposed with the help of the proposed operators. Lastly, a practical application of proposed model is given to verify the developed model and related results of the proposed model is compared with the results of the existing models to indicate its applicability.     

陕西田市镇及周边地质环境质量评价模型的构建及应用研究

鉴于地质环境质量在城市建设管理与规划中的重要性,文章从水文地质、工程地质和环境地质3个方面,确定了田市镇及周边地区地质环境质量评价指标体系,采用熵权法确定指标权重,构建了模糊加权模式评价模型,对田市镇及周边地区地质环境质量进行了评价与分区,并提出了针对性建议,以期为探索合适的评价方法及推进城镇建设的可持续发展提供地学依据。     

Clustering biomedical and gene expression datasets with kernel density and unique neighborhood set based vein detection

It is a crucial need for a clustering technique to produce high-quality clusters from biomedical and gene expression datasets without requiring any user inputs. Therefore, in this paper we present a clustering technique called KUVClust that produces high-quality clusters when applied on biomedical and gene expression datasets without requiring any user inputs. The KUVClust algorithm uses three concepts namely multivariate kernel density estimation, unique closest neighborhood set and vein-based clustering. Although these concepts are known in the literature, KUVClust combines the concepts in a novel manner to achieve high-quality clustering results. The performance of KUVClust is compared with established clustering techniques on real-world biomedical and gene expression datasets. The comparisons were evaluated in terms of three criteria (purity, entropy, and sum of squared error (SSE)). Experimental results demonstrated the superiority of the proposed technique over the existing techniques for clustering both the low dimensional biomedical and high dimensional gene expressions datasets used in the experiments.     

基于凸闭包集的不规则三角网算法研究与实现

不规则三角网是影响土石方量计算的关键问题之一，虽然目前已有大量研究人员对Delaunay三角网进行优化，但仍然缺少对三角网生成速率和计算精度的综合研究。针对此问题，基于VB.NET平台的点、边以及三角形的结构化数据结构设计快速生成凸闭包集形成Delaunay三角网，通过四边形空圆特性以及对角线最短原则对构网结果进行检验和优化。试验计算表明，该算法的计算结果与经典DTM法、方格网法的土方量计算相对误差均在0.30%以内。该方法不仅具有较高的组网效率，并能保持较好的计算精度。     

Retracted: State estimation of Takagi-Sugeno fuzzy system in networked control system with stochastic time delays under unknown attacks

This article concerns the event-triggered fuzzy filter design for Takagi-Sugeno (T-S) fuzzy systems subject to deception attacks under the stochastic multiple time-varying delays. A sequence of random variables, which are mutually independent but obey the Bernoulli distribution, is introduced to account for the randomly occurring communication delays. In order to efficiently utilize limited network communication bandwidth resources, the event-triggering scheme is adopted. A fuzzy filter with the attacked input signal is presented. Moreover, due to communication delays caused by event-triggering schemes and transmission, the filter adopts non-synchronous premise variables with the system. Then, by utilizing a model transformation technique, the fuzzy systems are developed. Furthermore, using the piecewise Lyapunov functional method technique, the resulting criterion provides sufficient conditions to ensure that fuzzy systems under deception attacks are stochastically stable with an H_∞ performance. Accordingly, the conditions for the co-design of the fuzzy filter and event-triggering schemes are given. Finally, numerical simulation with the industrial process provided to verify the proposed event-triggered design.     

Sensor network design based on system-wide reliability criteria. Part I: Objectives

Reliability based criteria are quite popular for optimal sensor network design. We present a modified definition of system reliability for sensor network design for two applications: reliable estimation of variables in a steady state linear flow process, and reliable fault detection and diagnosis for any process. Unlike the weakest-link based definition of system reliability in the literature, the proposed definition considers the entire system and is consistent with the reliability concept used in classical reliability literature. For each application, dual approaches for defining system reliability are proposed, and their analogy with the reliability problem in the classical reliability literature is established. Using examples and stochastic simulations, the advantage of using the proposed system reliability in contrast to the existing definition is illustrated. Part II of this series of articles presents methods for efficient generation of the system reliability function and its use in optimization-based approaches for designing optimal sensor networks.     

Asynchronous adaptive output feedback sliding mode control for Takagi-Sugeno fuzzy Markovian jump systems with actuator faults

In this article, the problem of asynchronous adaptive dynamic output feedback sliding mode control (SMC) for a class of Takagi-Sugeno (T-S) fuzzy Markovian jump systems (MJSs) with actuator faults is investigated. The asynchronous dynamic output feedback control strategy is employed, as the nonsynchronization phenomenon of jump modes exists between the plant and the controller. A novel asynchronous adaptive SMC approach is proposed to solve the synthesis problem for T-S fuzzy MJSs with actuator faults. Sufficient conditions for stochastic asymptotic stability of T-S fuzzy MJSs are given. Under the designed asynchronous adaptive SMC scheme, the effects of actuator faults and external disturbance can be completely compensated and the reachability of sliding surface is ensured. Finally, an example is provided to demonstrate the effectiveness of the proposed design techniques.     

采用磁流变阻尼器的人体座椅悬架系统模糊ANFIS-PID复合控制技术研究

对座椅悬架用单出杆式磁流变阻尼器进行阻尼特性试验,并借助MATLAB多项式拟合工具箱对改进多项式模型中未知参数进行辨识。分析人体振动特性,建立五自由度人体座椅悬架模型。综合模糊控制器与自适应模糊神经推理系统(Adaptive Neuro-Fuzzy Inference System,ANFIS)整定PID控制器的优点,提出一种模糊ANFIS-PID复合控制策略。采用正弦信号作为外界激励,分别对被动悬架、传统模糊控制、ANFIS-PID控制及模糊ANFIS-PID复合控制人体座椅悬架系统进行仿真分析。结果表明,辨识得出的参数和建立的改进多项式模型均可满足后续仿真要求;采用模糊ANFIS-PID复合控制策略的隔振效果明显优于传统模糊控制及ANFIS-PID控制,能有效改善人体座椅悬架系统的行驶平顺性及驾乘人员乘坐舒适性。     

Novel fuzzy adaptive finite-time nonsmooth control for uncertain nonlinear systems

In this article, a novel fuzzy adaptive finite-time nonsmooth controller is developed to handle the finite-time tracking problem for a class of uncertain nonlinear systems. Different from traditional fuzzy adaptive approximation methods, proposed method contains only one adaptive parameter, no matter how many states there are in the system. By constructing a new Lyapunov function with prescribed performance bound, the transient and steady performances of control system can be ensured. Further, based on a criterion of finite-time semiglobal practical stability and backstepping technology, a novel fuzzy adaptive finite-time nonsmooth control method is designed. It can be demonstrated that proposed control can effectively ensure tracking error tends to small neighborhood in a finite time. Finally, two examples have been simulated by the proposed control method, and it shows effective tracking performance.