Artificial Neural Network and Fuzzy Logic Based Techniques for Numerical Modeling and Prediction of Aluminum-5%Magnesium Alloy Doped with REM Neodymium

In this study, the mechanical properties of aluminum-5%magnesium doped with rare earth metal neodymium were evaluated. Fuzzy logic (FL) and artificial neural network (ANN) were used to model the mechanical properties of aluminum-5%magnesium (0-0.9 wt%) neodymium. The single input (SI) to the fuzzy logic and artificial neural network models was the percentage weight of neodymium, while the multiple outputs (MO) were average grain size, ultimate tensile strength, yield strength elongation and hardness. The fuzzy logic-based model showed more accurate prediction than the artificial neutral network-based model in terms of the correlation coefficient values (R).

Optimum Design for the Magnification Mechanisms Employing Fuzzy Logic-ANFIS

To achieve high work performance for compliant mechanisms of motion scope,continuous work condition,and high frequency,we propose a new hybrid algorithm that could be applied to multi-objective optimum design.In this investigation,we use the tools of finite element analysis(FEA)for a magnificationmechanism to find out the effects of design variables on the magnification ratio of the mechanism and then select an optimal mechanism that could meet design requirements.A poly-algorithm including the Grey-Taguchi method,fuzzy logic system,and adaptive neuro-fuzzy inference system(ANFIS)algorithm,was utilized mainly in this study.The FEA outcomes indicated that design variables have significantly affected on magnification ratio of the mechanism and verified by analysis of variance and analysis of the signal to noise of grey relational grade.The results are also predicted by employing the tool of ANFIS in MATLAB.In conclusion,the optimal findings obtained:Its magnification is larger than 40 times in comparison with the initial design,the maximum principal stress is 127.89MPa,and the first modal shape frequency obtained 397.45 Hz.Moreover,we found that the outcomes obtained deviation error compared with predicted results of displacement,stress,and frequency are 8.76%,3.6%,and 6.92%,respectively.

Using a 3D Interval Type-2 Fuzzy Interpolation System to Improve Modeless Robots Measurement and Calibration Accuracy

This paper is an extended research for a novel technique used in the pose error compensations of the robot and manipulator calibration process based on an IT2FEI （interval type-2 fuzzy error interpolation） method. Robot calibrations can be classified into model-based and modeless methods. A model-based calibration method normally requires that the practitioners understand the kinematics of the robot therefore may pose a challenger for field engineers. An alternative yet effective means for robot calibration is to use a modeless method; however with such a method there is a conflict between the calibration accuracy of the robot and the number of grid points used in the calibration task. In this paper, an interval type-2 fuzzy interpolation system is applied to improve the compensation accuracy of the robot in its 3D workspace. An on-line type-2 fuzzy inference system is implemented to meet the needs of on-line robot trajectory planning and control. The simulated results given in this paper show that not only robot compensation accuracy can be greatly improved, but also the calibration process can be significantly simplified, and it is more suitable for practical applications.

Fuzzy User Access Trust Model for Cloud Access Control

Cloud computing belongs to a set of policies,protocols,technologies through which one can access shared resources such as storage,applications,net-works,and services at relatively low cost.Despite the tremendous advantages of cloud computing,one big threat which must be taken care of is data security in the cloud.There are a dozen of threats that we are being exposed to while avail-ing cloud services.Insufficient identity and access management,insecure inter-faces and Applications interfaces(APIs),hijacking,advanced persistent threats,data threats,and many more are certain security issues with the cloud platform.APIs and service providers face a huge challenge to ensure the security and integ-rity of both network and data.To overcome these challenges access control mechanisms are employed.Traditional access control mechanisms fail to monitor the user operations on the cloud platform and are prone to attacks like IP spoofing and other attacks that impact the integrity of the data.For ensuring data integrity on cloud platforms,access control mechanisms should go beyond authentication,identification,and authorization.Thus,in this work,a trust-based access control mechanism is proposed that analyzes the data of the user behavior,network beha-vior,demand behavior,and security behavior for computing trust value before granting user access.The method that computes thefinal trust value makes use of the fuzzy logic algorithm.The trust value-based policies are defined for the access control mechanism and based on the trust value outcome the access control is granted or denied.

Application of fuzzy logic for determining of coal mine mechanization

The fundamental task of mining engineers is to produce more coal at a given level of labour input and material costs, for optimum quality and maximum efficiency. To achieve these goals, it is necessary to automate and mechanize mining operations. Mechanization is an objective that can result in significant cost reduction and higher levels of profitability for underground mines. To analyze the potential of mechanization, some important factors such as seam inclination and thickness, geological disturbances, seam floor conditions and roof conditions should be considered. In this study we have used fuzzy logic, membership functions and created fuzzy rule-based methods and considered the ultimate objective： mechanization of mining. As a case study, the mechanization of the Tazare coal seams in Shahroud area of Iran was investigated. The results show a low potential for mechanization in most of the Tazare coal seams.

A psychophysical-based fuzzy image interpolation algorithm

To produce a smoother and more natural interpolated image, and to preserve and enhance original image details, we defined three perception-based local statistic parameters, namely contrast, noise visibility, and edge strength based on three psychophysical principles, including Weber’s Law, Fechner’s Law, and Stevens’ Power Law, and integrated these parameters into a fuzzy logic system to set up an advanced image interpolation algorithm. Application of this algorithm to detect edge behaviors and local statistical information of images demonstrated better noise removal ability and obtained sharper edges than traditional image interpolation algorithems such as nearest neighbor, bilinear and bicubic interpolation methods.

Slow driving control of tracked vehicles with automated mechanical transmission based on fuzzy logic

In order to move tracked vehicles at an extremely slowspeed with automated mechanical transmission（ AMT）,slowdriving function was added in the original system. The principle and requirement of slowdriving function were analyzed. Based on analysis of slow driving characteristic,identification of slowdriving condition and fuzzy control algorithm,a control strategy of the clutch was designed. In order to realize slowdriving,the clutch was controlled in a slipping mode as manual driving. The vehicle speed was increased to a required speed and kept in a small range by engaging or disengaging the clutch to the approximate half engagement point. Based on the control strategy,a control software was designed and tested on a tracked vehicle with AMT. The test results showthat the control of the clutch with the slowdriving function was smoother than that with original systemand the vehicle speed was slower and steadier.

多重制度逻辑下新型农村集体经济的治理机制研究——基于模糊集定性比较分析

新型农村集体经济是实现中国式现代化的重要微观经济基础。新时代下,新型农村集体经济发展面临前所未有的机遇与挑战,如何构建长效治理机制,推动新型农村集体经济高质量发展成为各界关注的热点。基于制度逻辑理论,运用模糊集定性比较分析法(fsQ-CA),以农业农村部第三批16个乡村治理典型示范县为案例对象,对农村集体经济发展现状和治理模式展开分析,深入探讨了多重制度逻辑共存前提下新型农村集体经济治理的几种有效机制及其内涵,并对每种机制的应用场景进行案例验证检视,进而提出进一步夯实新型农村集体经济治理的基层组织,推动村规民约与乡村社会网络融合互嵌,以及加快构建多主体共治共建共享的新型农村集体经济治理新格局的政策建议。

不确定非线性离散系统指令滤波事件触发控制

本文提出了考虑输入饱和的一类不确定非线性离散系统的事件触发指令滤波控制方法.采用指令滤波控制技术解决了传统反步法存在的“因果矛盾”问题,引入补偿机制提高了系统的控制精度;利用事件触发机制能够避免自适应律和控制律的频繁更新,降低了计算负担,提高了资源利用率;运用模糊逻辑系统逼近系统中未知的非线性函数;结合李雅普诺夫稳定性理论,验证了提出的控制方案能够保证跟踪误差收敛到原点小的邻域内以及闭环系统的所有信号有界.仿真结果表明,本文提出的控制方法具有较强的鲁棒性及较好的跟踪性能.

n阶系统的fuzzy推理建模方法研究

把fuzzy推理建模法用于对一般n阶系统的建模中,分别建立了研究对象的输入输出型模型和状态空间型模型.在此基础上,对fuzzy推理建模法做了进一步的推广,分别选用梯形和"三角波"型fuzzy集隶属函数构造系统的微分方程模型,并通过仿真试验对两种模型进行了比较.

边缘线性化方法构造的Fuzzy系统及其逼近性能分析

给出了基于边缘线性化方法构造的Fuzzy系统输出函数的一般表达式,揭示了该方法的插值机理,证明了由其构造的Fuzzy系统输出函数可归结为插值函数的形式,在此基础上,分析了该方法所构造的Fuzzy系统的逼近误差。仿真实验表明,边缘线性化方法构造的Fuzzy系统对非线性连续函数具有很高的逼近精度。

省级政府环境治理绩效的组态生成机制研究——基于多重制度逻辑的动态FSQCA分析

省级政府承担着改善地区环境质量,守护一方绿水青山的重任。探究不同制度条件下地方政府环境治理绩效的组态生成机制,对加强生态文明建设,促进人与自然和谐共生的中国式现代化具有重要意义。基于多重制度逻辑理论,本文对30个省级行政区2017—2019年的数据进行了动态的汇总型FSQCA分析。研究发现:单一条件不能构成省级政府环境治理高绩效的必要生成条件;生成省级政府环境治理高绩效的组态机制共有四种,分别为:数据赋能单向机制、市场—法治协同机制、市场—公众协同机制以及政治—数据协同机制。进一步分析表明,在我国不同地区,生成省级政府环境治理高绩效的组态机制具有差异性。

基于模糊逻辑的空调系统广义预测控制参数整定

针对多输入多输出(multiple-input-multiple-output,MIMO)变风量空调系统(variable air volume,VAV),本文提出了一种基于模糊逻辑和事件触发的广义预测控制(generalized predictive control,GPC)参数整定方法。针对多变量VAV空调系统,基于输出斜率等新型模糊目标参数及高斯双边形隶属度函数建立模糊预测模型,提高了隶属函数拟合度以更贴切地反映系统当前时刻状态。利用麻雀智能算法(sparrow search algorithm,SSA)构建隶属度函数,对GPC控制器中的柔化因子和加权系数进行在线分段整定,有效提升了系统性能。此外,在参数整定过程中,引入事件触发机制(event-triggered mechanism,ETM),在保证控制性能的同时,避免了不必要的控制器采样与更新,降低了系统在线计算量并减少了能源消耗。最后通过VAV空调系统仿真实验验证,证明了本文提出方法的可行性和有效性。

OPTIMAL TORQUE CONTROL STRATEGY FOR PARALLEL HYBRID ELECTRIC VEHICLE WITH AUTOMATIC MECHANICAL TRANSMISSION

In parallel hybrid electrical vehicle （PHEV） equipped with automatic mechanical transmission （AMT）, the driving smoothness and the clutch abrasion are the primary considerations for powertrain control during gearshift and clutch operation. To improve these performance indexes of PHEV, a coordinated control system is proposed through the analyzing of HEV powertrain dynamic characteristics. Using the method of minimum principle, the input torque of transmission is optimized to improve the driving smoothness of vehicle. Using the methods of fuzzy logic and fuzzy-PID, the engaging speed of clutch and the throttle opening of engine are manipulated to ensure the smoothness of clutch engagement and reduce the abrasion of clutch friction plates. The motor provides the difference between the required input torque of transmission and the torque transmitted through clutch plates. Results of simulation and experiments show that the proposed control strategy performs better than the contrastive control system, the smoothness of driving and the abrasion of clutch can be improved simultaneously.

Design and Analysis of Electro-mechanical Hybrid Anti-lock Braking System for Hybrid Electric Vehicle Utilizing Motor Regenerative Braking

Braking on low adhesion-coefficient roads, hybrid electric vehicle＇s motor regenerative torque is switched off to safeguard the normal anti-lock braking system （ABS） fimction. When the ABS control is terminated, the motor regenerative braking is readmitted. Aiming at avoiding permanent cycles from hydraulic anti-lock braking to motor regenerative braking, a novel electro-mechanical hybrid anti-lock braking system using fuzzy logic is designed. Different from the traditional single control structure, this system has a two-layered hierarchical structure, The first layer is responsible for harmonious adjustment or interaction between regenerative system and anti-lock braking system. The second layer is responsible for braking torque distribution and adjustment. The closed-loop simulation model is built. Control strategy and method for coordination between regenerative and anti-lock braking are developed. Simulation braking on low adhesion-coefficient roads with fuzzy logic control and real vehicle braking field test are presented. The results from simulating analysis and experiment show braking performance of the vehicle is perfect, harmonious coordination between regenerative and anti-lock braking function, significant amount of braking energy can be recovered and the proposed control strategy and method are effective.

模糊逻辑引导的多粒度深度神经网络

准确识别和分类组织病理图像对于疾病的早期检测和治疗至关重要.病理医生诊断时通常会采用多层次的方式进行判断,即观察各个倍率下细胞的异常区域,然而现有模型通常只能在单一粒度上进行特征提取,忽略细胞的多粒度特性.因此,文中提出模糊逻辑引导的多粒度深度神经网络.首先,针对细胞的构造,在粗粒度、中粒度和细粒度这3个粒度层面上进行多粒度特征提取,充分利用病理组织图像中的信息.同时,针对多粒度特征提取时出现的关键信息冗余问题,引入模糊逻辑理论,设置多个模糊隶属函数,分别描述细胞在不同角度上的特征.然后,通过模糊运算得到模糊通用特征,由此设计模糊逻辑引导的交叉注意力机制模块,实现模糊通用特征对多粒度特征的引导.最后,通过编码器将特征扩散至所有的补丁令牌,获得较好的分类准确性和鲁棒性.实验表明,文中网络在组织病理图像分类上具有较高的准确率.

Fuzzy logic systems are equivalent to feedforward neural networks

Fuzzy logic systems and feedforward neural networks are equivalent in essence. First, interpolation representations of fuzzy logic systems are introduced and several important conclusions are given. Then three important kinds of neural networks are defined, i.e. linear neural networks, rectangle wave neural networks and nonlinear neural networks. Then it is proved that nonlinear neural networks can be represented by rectangle wave neural networks. Based on the results mentioned above, the equivalence between fuzzy logic systems and feedforward neural networks is proved, which will be very useful for theoretical research or applications on fuzzy logic systems or neural networks by means of combining fuzzy logic systems with neural networks.

Modelling on fuzzy control systems

A kind of modelling method for fuzzy control systems is first proposed here, which is called modelling method based on fuzzy inference (MMFI). It should be regarded as the third modelling method that is different from two well-known modelling methods, that is, the first modelling method, mechanism modelling method (MMM), and the second modelling method, system identification modelling method (SIMM). This method can, based on the interpolation mechanism on fuzzy logic system, transfer a group of fuzzy inference rules describing a practice system into a kind of nonlinear differential equation with variable coefficients, called HX equations, so that the mathematical model of the system can be obtained. This means that we solve the difficult problem of how to get a model represented as differential equations on a complicated or fuzzy control system.

知识和数据驱动的多时间尺度采样系统建模方法

针对多时间尺度采样系统快采样变量的信息难以充分利用,建模信息不足的问题,提出一种知识和数据驱动的多时间尺度采样系统的模糊迁移学习建模方法,建立多时间尺度采样系统模型,提高建模精度.首先,设计滤波插补方法填补慢采样变量的空缺值,统一慢采样变量和快采样变量为慢时间尺度,利用慢时间尺度变量建立目标模型.其次,提出模型共享机制补充目标模型的建模信息,统一慢采样变量和快采样变量为快时间尺度,利用快时间尺度变量建立参考模型,将参考模型中充足的模型知识迁移到目标模型中.最后,利用参考模型的知识和目标模型的数据学习目标模型的参数,提高目标模型的精度.将提出的建模方法应用于理论数据集,实验证明该方法可以充分挖掘建模信息,建立高精度的多时间尺度采样系统模型.