Exploring Taiwan’s China Landscape Painting Aesthetic Preferences Through Evaluation Grid Method and the Continuous Fuzzy Kano Model

The primary objective of this study is to apply the Evaluation Grid Method(EGM)and the continuous fuzzy Kano quality model to explore the cognitive preferences of Taiwan China residents regarding the beauty of Taiwan’s China landscape paintings.The aim is to contribute to the development of social and cultural art and promote the widespread appeal of art products.Through a literature review,consultations with aesthetic experts,and the application of Miryoku Engineering’s EGM,this paper consolidates the factors that contribute to the attractiveness of painting art products among Taiwan China residents,taking into account various aesthetic qualities.Simultaneously,the paper introduces the use of the triangular fuzzy golden ratio scale semantics,specifically the equal-ratio aesthetic scale semantics,as a replacement for the traditional subjective consciousness model.Departing from the traditional discrete Kano model that employs the mode as the standard for evaluating quality,this study applies triangular fuzzy numbers to the continuous Kano quality model to analyze the diverse preferences and evaluation standards of the public.The hope is that this research methodology will not only deepen Taiwan China residents’understanding and aesthetic literacy of painting art but also serve as a reference for the popularization of art products.

车辆主动悬架自适应变论域T-S模糊控制研究

针对传统变论域模糊控制存在过度依赖专家经验、伸缩因子参数不能自适应调整的问题,提出一种车辆主动悬架自适应变论域T-S模糊控制策略,从而提高车辆的行驶平顺性。结合神经网络和T-S模糊推理建立基于自适应神经模糊推理的一阶T-S模糊控制器,利用神经网络的自学习特性产生完善的模糊规则,进而在传统函数型伸缩因子的基础上,将系统误差和误差变化率作为动态参数引入伸缩因子中,实现伸缩因子参数的自适应调整,解决了传统函数型伸缩因子因参数确定难度大导致控制效果差的问题。通过随机工况下的仿真分析和基于相似理论的缩尺实验,对所提出算法的有效性和工况自适应性进行了验证。结果表明,所提出的自适应变论域T-S模糊控制策略具有较强的工况适应性,在不同车速、路面激励下均可有效提高车辆的平顺性并保证轮胎接地安全性。

基于量子衍生涡流算法和T⁃S模糊推理模型的储层岩性识别

鉴于梯度下降法易陷入局部极值、普通群智能优化算法易早熟收敛,提出一种基于量子衍生涡流算法(Quantum Vortex Search Algorithm,QVSA)和T⁃S模糊推理模型的岩性识别方法,QVSA具有操作简单、收敛速度快、寻优能力强等优点,有助于T⁃S模糊推理模型获得最优参数配置,从而实现储层岩性的准确识别。首先利用具有全局搜索能力的QVSA优化T⁃S模糊推理模型的各种参数;然后利用主成分分析方法降低获取的地震属性维度;再利用优化的T⁃S模糊推理模型识别储层岩性。实验结果表明,利用反映储层特征的8个地震属性识别储层岩性时,所提方法的识别正确率达到92%,比普通BP网络方法高5.1%,同时查准率、查全率、F1分数等指标也较BP网络方法提升明显。

基于T-S模糊故障树的驱动电机冷却系统可靠性分析

车用驱动电机液压冷却系统存在故障机理不确定等问题。本文应用模糊数描述其故障概率和故障程度,建立驱动电机冷却系统的T-S模糊故障树模型,计算系统模糊可能性,分析各部件的概率重要度和关键重要度,找出影响系统可靠性的关键部件。

混合差分和多种群粒子群算法的T-S模糊模型辨识

为提高T-S模型的辨识精度,针对基本粒子群优化(particle swarm optimization,PSO)算法T-S模型全局优化辨识问题,提出混合差分和多种群粒子群算法的T-S模糊模型辨识方法,将T-S模型前件参数和后件参数整体编码进行全局优化辨识.为避免基本粒子群的早熟收敛和后期收敛速度慢的缺陷对T-S模型辨识精度和速度的影响,算法将种群分为若干个子群,每个子群根据粒子适应度值自适应调整惯性权重,平衡了算法的开发和探索能力,对子群最优粒子,进行差分操作以增强算法的全局搜索能力,采用全局最优粒子替代随机子群的最优粒子以加强子群间的信息交流,维持粒子多样性.典型非线性系统和混沌系统的仿真结果表明,采用混合差分和多种群粒子群算法辨识的T-S模型具有更高的辨识精度.

S-T-S混合式循环互助教学模式用于少学时“化工原理”课程

“化工原理”课程是化工及相关专业的专业基础课程,相关专业的学时少,课程难,需重点解决的问题是“三多”“三不愿”“三不明”。针对痛点问题,提出了线上线下混合式循环互助的创新设计思路,包括:教师帮助学生自制课前、课中、课后习题,学生帮助教师反馈真实学习情况,学生帮助学生组团互助学习。问卷调查和评教结果显示,在一定程度上解决了“三多”“三不愿”的问题;通过建立“助教团队”反馈不同层次学生的学习情况,较好地解决了“三不明”的问题。学生的成绩与教改前相比有明显提高。

基于T-S模糊逻辑的混合储能孤岛直流微电网功率分配控制

在含混合储能的直流微电网中,传统阻容下垂控制无法解决由线路电阻和负荷功率波动导致的系统功率分配失衡问题。为此,提出一种基于Takagi-Sugeno(T-S)模糊逻辑的自适应阻容下垂控制方法以实现混合储能的分频分配。根据蓄电池和超级电容的物理特性,建立单个蓄电池支路输出电压、蓄电池组间输出功率差额和阻性下垂系数之间的T-S模糊逻辑关系,以及单个超级电容支路输出功率及其变化率、超级电容组间功率差额和容性下垂系数之间的T-S模糊逻辑关系,并由此构建基于T-S模糊逻辑的阻容下垂控制器。推导含混合储能的直流微电网中各部分的平均阻抗模型,并采用阻抗比分析法对微电网的小信号稳定性进行研究。MATLAB/Simulink仿真结果表明,基于T-S模糊逻辑的阻容下垂控制可保证在线路电阻和负荷功率波动情况下系统功率的合理分配。

基于改进T-S模糊模型的弧焊机器人轨迹实时跟踪

针对弧焊机器人在实际作业中受到诸多不确定因素的影响,导致弧焊机器人焊接轨迹偏离预期轨迹的问题,提出一种基于改进T-S(Takagi-Sugeno)模糊模型的弧焊机器人轨迹实时跟踪方法。采用图像识别技术识别并定位焊缝,通过坐标转换,确定焊缝中心线在机器人坐标系中的位置坐标。利用蚁群算法根据焊缝中心线在机器人坐标系中的坐标,搜索从起始位置到目标位置的最优焊接路径。设计了一种改进区间二型T-S模糊模型,通过视觉传感器实时获取新的焊缝信息,计算轨迹误差,以此为输入,利用改进T-S模糊模型对机器人的运动轨迹进行实时调整。实验结果表明,采用改进T-S模糊模型后,机器人轨迹跟踪离散度均降至1 mm以下,弧焊机器人轨迹与预期轨迹高度重合,比传统方法跟踪离散度大大降低。

基于T-S模糊建模的气动肌肉拮抗关节预测角度控制

目的:为了提升气动肌肉拮抗关节的控制精度。方法:提出了一种用于气动肌肉拮抗关节角度跟踪的T-S模糊模型预测控制方法。建立了拮抗关节的T-S模糊模型,并对模型进行了离散化处理;基于离散后的T-S模糊模型,设计了拮抗关节的模型预测控制器,通过特征值法验证了系统的稳定性。结果:仿真实验表明,T-S模糊模型预测控制器跟踪±15°和±10°的角度时,相对于传统模型预测控制,平均绝对误差分别降低了29.05%和44.11%。结论:设计的T-S模糊模型预测控制器提高了气动肌肉拮抗关节的控制精度。

T-S模糊系统的动态自触发有限时间H_(∞)控制

文中研究一类不确定Takagi-Sugeno(T-S)模糊系统的自触发有限时间H_(∞)控制。首先,提出动态自触发方案,构造触发参数的动力学方程,从而使自触发机制能依据触发时刻状态自适应地调整触发参数;同时,基于当前触发时刻的系统状态实时计算触发间隔,预测下一个触发时刻。相较于T-S模糊系统的静态自触发方案,文中所提的动态自触发方案能进一步降低触发频率,节约计算资源。其次,基于有限时间稳定理论,设置系统状态运动轨迹的次临界界限;结合状态轨迹与次临界界限的相对位置关系和动态自触发方案,分析系统的H_(∞)性能;此外,设计依赖模糊规则的H_(∞)状态反馈控制律,保证T-S模糊系统的动力学行为有限时间有界。最后,给出实例仿真验证文中方法的可行性。

制冷站系统负荷和能效比改进T-S模糊模型构建

制冷站具有非线性、强耦合等特点,导致传统机理的建模面临困难。为实现系统节能优化控制,并改善模型在线修正和移植性能,提出一种基于改进Takagi-Sugeno(T-S)模糊模型的制冷站负荷和能效比动态建模方法,前件结构辨识设计改进天牛须搜索算法,以改善模糊C-均值聚类方法对初值敏感和易陷入局部最优的问题;为实现非线性模型辨识,并降低现场测试数据噪声的影响,设计自适应扩展卡尔曼滤波算法实现模型后件参数辨识和在线修正。实验结果表明,所建立的负荷和能效比预测模型在广州某建筑上运行时,相对误差分别为0.63%和1.49%;使用广州市另一座建筑的数据进行模型可移植性验证,经过500步在线训练,新模型成功收敛,证明所构建模型具备良好的可移植性和适应性。

T-S模糊系统的自触发有限时间H∞控制

为克服事件触发控制对硬件设备的依赖以及控制策略频繁更新的局限,针对一类不确定Takagi-Sugeno(T-S)模糊系统的有限时间H∞控制问题,引入自触发机制,提出一种基于此机制下只与触发时刻状态有关的模糊控制器设计方法;基于自触发机制并结合有限时间控制的思想,通过设计触发间隔函数、推导并求解线性矩阵不等式(LMI),使得系统在保障稳定性的前提下,系统运动轨迹不会超过一定的界限,并达到所期望H∞性能水平;同时相较于事件触发机制,自触发机制位于系统内部,无需额外硬件设备接收触发信号且只需利用当前的触发状态信息来预测下一触发时刻,在一定程度上进一步降低系统资源成本;此外,利用李亚普诺夫函数对系统稳定性进行了严格分析,并提供一个确保能够防止芝诺现象发生的证明;最后,通过数值仿真、单连杆刚性机器人系统实例仿真两个实验说明所提方法的有效性。

基于T-S模糊神经网络的光伏发电机组自动控制

光照情况变化会使光伏发电机组功率呈现不稳定性,加大光伏发电机组控制难度,为此,设计了基于T-S模糊神经网络的光伏发电机组自动控制方法。构建光伏阵列数学模型,分析在均匀和不均匀2种光照情况下光伏发电机组特性曲线。以分析结果为依据,采用T-S模糊神经网络构建光伏发电机组自动控制模型。为保证良好的控制效果,引入定比因子优化隶属度函数,输出最佳跟踪结果,结合最佳跟踪结果和自动控制模型实现光伏发电机组自动控制。测试结果显示,该方法能够完成光伏阵列特性分析,控制效果好。

Synchronous Control of Complex Networks with Fuzzy Connections

This article is based on the T-S fuzzy control theory and investigates the synchronization control problem of complex networks with fuzzy connections. Firstly, the main stability equation of a complex network system is obtained, which can determine the stability of the synchronous manifold. Secondly, the main stable system is fuzzified, and based on fuzzy control theory, the control design of the fuzzified main stable system is carried out to obtain a coupling matrix that enables the complex network to achieve complete synchronization. The numerical analysis results indicate that the control method proposed in this paper can effectively achieve synchronization control of complex networks, while also controlling the transition time for the network to achieve synchronization.

Constrained predictive control based on T-S fuzzy model for nonlinear systems

A constrained generalized predictive control （GPC） algorithm based on the T-S fuzzy model is presented for the nonlinear system. First, a Takagi-Sugeno （T-S） fuzzy model based on the fuzzy cluster algorithm and the orthogonalleast square method is constructed to approach the nonlinear system. Since its consequence is linear, it can divide the nonlinear system into a number of linear or nearly linear subsystems. For this T-S fuzzy model, a GPC algorithm with input constraints is presented. This strategy takes into account all the constraints of the control signal and its increment, and does not require the calculation of the Diophantine equations. So it needs only a small computer memory and the computational speed is high. The simulation results show a good performance for the nonlinear systems.

A unified approach to fuzzy modelling and robust synchronization of different hyperchaotic systems

In this paper, a Takagi Sugeno （T-S） fuzzy model-based method is proposed to deal with the problem of synchronization of two identical or different hyperchaotic systems. The T S fuzzy models with a small number of fuzzy IF-THEN rules are employed to represent many typical hyperchaotic systems exactly. The benefit of employing the T-S fuzzy models lies in mathematical simplicity of analysis. Based on the T-S fuzzy hyperchaotic models, two fuzzy controllers arc designed via parallel distributed compensation （PDC） and exact linearization （EL） techniques to synchronize two identical hyperchaotic systems with uncertain parameters and two different hyperchaotic systems, respectively. The sufficient conditions for the robust synchronization of two identical hyperchaotic systems with uncertain parameters and the asymptotic synchronization of two different hyperchaotic systems are derived by applying the Lyapunov stability theory. This method is a universal one of synchronizing two identical or different hyperchaotic systems. Numerical examples are given to demonstrate the validity of the proposed fuzzy model and hyperchaotic synchronization scheme.

Predictive functional control based on fuzzy T-S model for HVAC systems temperature control

In heating, ventilating and air-conditioning （HVAC） systems, there exist severe nonlinearity, time-varying nature, disturbances and uncertainties. A new predictive functional control based on Takagi-Sugeno （T-S） fuzzy model was proposed to control HVAC systems. The T-S fuzzy model of stabilized controlled process was obtained using the least squares method, then on the basis of global linear predictive model from T-S fuzzy model, the process was controlled by the predictive functional controller. Especially the feedback regulation part was developed to compensate uncertainties of fuzzy predictive model. Finally simulation test results in HVAC systems control applications showed that the proposed fuzzy model predictive functional control improves tracking effect and robustness. Compared with the conventional PID controller, this control strategy has the advantages of less overshoot and shorter setting time, etc.

Controller Design for Electric Power Steering System Using T-S Fuzzy Model Approach

Pressure ripples in electric power steering （EPS） systems can be caused by the phase lag between the driver s steering torque and steer angle, the nonlinear frictions, and the disturbances from road and sensor noise especially during high-frequency maneuvers. This paper investigates the use of the robust fuzzy control method for actively reducing pressure ripples for EPS systems. Remarkable progress on steering maneuverability is achieved. The EPS dynamics is described with an eight-order nonlinear state-space model and approximated by a Takagi-Sugeno （T-S） fuzzy model with time-varying delays and external disturbances. A stabilization approach is then presented for nonlinear time-delay systems through fuzzy state feedback controller in parallel distributed compensation （PDC） structure. The closed-loop stability conditions of EPS system with the fuzzy controller are parameterized in terms of the linear matrix inequality （LMI） problem. Simulations and experiments using the proposed robust fuzzy controller and traditional PID controller have been carried out for EPS systems. Both the simulation and experiment results show that the proposed fuzzy controller can reduce the torque ripples and allow us to have a good steering feeling and stable driving.

Statistic PID Tracking Control for Non-Gaussian Stochastic Systems Based on T-S Fuzzy Model

A new robust proportional-integral-derivative （PID） tracking control framework is considered for stochastic systems with non-Gaussian variable based on B-spline neural network approximation and T-S fuzzy model identification. The tracked object is the statistical information of a given target probability density function （PDF）, rather than a deterministic signal. Following B-spline approximation to the integrated performance function, the concerned problem is transferred into the tracking of given weights. Different from the previous related works, the time delay T-S fuzzy models with the exogenous disturbances are applied to identify the nonlinear weighting dynamics. Meanwhile, the generalized PID controller structure and the improved convex linear matrix inequalities （LMI） algorithms are proposed to fulfil the tracking problem. Furthermore, in order to enhance the robust performance, the peak-to-peak measure index is applied to optimize the tracking performance. Simulations are given to demonstrate the efficiency of the proposed approach.

Modeling and Stability Analysis for Non-linear Network Control System Based on T-S Fuzzy Model

Based on the T-S fuzzy model,this paper presents a new model of non-linear network control system with stochastic transfer delay.Sufficient criterion is proposed to guarantee globally asymptotically stability of this two-levels T-S fuzzy model.Also a T-S fuzzy observer of NCS is designed base on this two-levels T-S fuzzy model.All these results present a new approach for networked control system analysis and design.