Eliciting and measuring hesitance in decision-making

Hesitance is an innate human psychological phenomenon pervasive in our daily life. Although it is very complex and still not fully understood, there have been many studies related to this interesting topic. In this study, we proposed two fundamental problems about hesitance: the first is the generating problem, which is related to how to elicit hesitance information from human thinking; the second is the measuring problem, which is related to how to devise effective and reasonable methods to measure hesitance degrees from given hesitance information. With these two fundamental questions, we first discussed and analyzed several examples in real life involving hesitance, showing that it can indeed be recorded and represented. Then we roughly classified the hesitance information into two major classes: static hesitance and dynamic hesitance. Some simple and interesting methods were proposed to elicit hesitance, and more reasonable methods were proposed to measure the hesitance degree from the two classes of hesitance information. All methods that have been discussed in this study try to avoid complexity in every aspect while reserving strictness and reasonability; therefore, the present study provides suitable information for practitioners with different backgrounds.     

Distance and similarity measures of Pythagorean fuzzy sets based on the Hausdorff metric with application to fuzzy TOPSIS

Pythagorean fuzzy sets (PFSs) were proposed by Yager in 2013 to treat imprecise and vague information in daily life more rigorously and efficiently with higher precision than intuitionistic fuzzy sets. In this paper, we construct new distance and similarity measures of PFSs based on the Hausdorff metric. We first develop a method to calculate a distance between PFSs based on the Hasudorff metric, along with proving several properties and theorems. We then consider a generalization of other distance measures, such as the Hamming distance, the Euclidean distance, and their normalized versions. On the basis of the proposed distances for PFSs, we give new similarity measures to compute the similarity degree of PFSs. Some examples related to pattern recognition and linguistic variables are used to validate the proposed distance and similarity measures. Finally, we apply the proposed methods to multicriteria decision-making by constructing a Pythagorean fuzzy Technique for Order Preference by Similarity to an Ideal Solution and then present a practical example to address an important issue related to social sector. Numerical results indicate that the proposed methods are reasonable and applicable and also that they are well suited in pattern recognition, linguistic variables, and multicriteria decision-making with PFSs.     

Type II fuzzy set-based data analytics to explore amino acid associations in protein sequences of Swine Influenza Virus

The veracity present in molecular data available in biological databases possesses new challenges for data analytics. The analysis of molecular data of various diseases can provide vital information for developing better understanding of the molecular mechanism of a disease. In this paper, an attempt has been made to propose a model that addresses the issue of veracity in data analytics for amino acid association patterns in protein sequences of Swine Influenza Virus. The veracity is caused by intra-sequential and inter-sequential biases present in the sequences due to varying degrees of relationships among amino acids. A complete dataset of 63,682 protein sequences is downloaded from NCBI and is refined. The refined dataset consists of 26,594 sequences which are employed in the present study. The type I fuzzy set is employed to explore amino acid association patterns in the dataset. The type I fuzzy support is refined to partially remove the inter-sequential biases causing veracity in data. The remaining inter-sequential biases present in refined fuzzy support are evaluated and eliminated using type II fuzzy set. Hence, it is concluded that a combination of type II fuzzy & refined fuzzy approach is the optimal approach for extracting a better picture of amino acid association patterns in the molecular dataset.     

四轮轮毂电机驱动电动汽车电液复合制动平顺性控制策略

液压制动与电机再生制动的时域响应差异导致电动汽车在制动模式切换时产生冲击感，影响驾驶员驾驶感受和乘坐舒适性。以四轮轮毂电机驱动电动汽车为研究对象，提出一种基于分层架构的电液复合制动平顺性控制策略。针对"高压蓄能器+电机泵"式电子液压制动系统（EHB），上层控制器提出基于模糊控制的轮缸压力控制策略；针对制动模式切换过程中产生的冲击，下层控制器提出包括液压介入预测模块和电机制动补偿模块的电液复合制动平顺性控制策略。通过Simulink-AMESim联合仿真平台进行仿真试验验证。结果表明，轮缸压力控制策略能够保证轮缸液压力较好地追随目标压力，且稳态误差不超过2%；电液复合制动平顺性控制策略能够有效提高制动系统的响应速度，同时显著降低制动模式切换时的冲击，能提升车辆制动平顺性和乘坐舒适性。     

Uncertain location–allocation decisions for a bi-objective two-stage supply chain network design problem with environmental impacts

In the cases that the historical data of an uncertain event is not available, belief degree-based uncertainty theory is a useful tool to reflect such uncertainty. This study focuses on uncertain bi-objective supply chain network design problem with cost and environmental impacts under uncertainty. As such network may be designed for the first time in a geographical region, this problem is modelled by the concepts of belief degree-based uncertainty theory. This article is almost the first study on belief degree-based uncertain supply chain network design problem with environmental impacts. Two approaches such as expected value model and chance-constrained model are applied to convert the proposed uncertain problem to its crisp form. The obtained crisp forms are solved by some multi-objective optimization approaches of the literature such as TH, Niroomand, MMNV. A deep computational study with several test problems are performed to study the performance of the crisp models and the solution approaches. According to the results, the obtained crisp formulations are highly sensitive to the changes in the value of the cost parameters. On the other hand, Niroomand and MMNV solution approaches perform better than other solution approaches from the solution quality point of view.     

基于计算机的镀液温度智能控制系统设计

为提高镀液温度的控制精度,以计算机为平台设计了一种镀液温度智能控制系统。设计了一种模糊PID温度控制器,可实现PID控制器参数的实时在线调整。利用动态矩阵,统计过去和当前时刻的偏差,预测未来偏差,进而得到最佳输入。最后,进行了实验研究。结果表明:镀液温度智能控制系统的收敛速率快、精度高、稳定性好,能满足电镀行业的需求,具有广阔的应用前景。     

基于互信息和PID神经网络的SCR脱硝扰动补偿控制

针对火电厂选择性催化还原(Selective Catalytic Reduction，SCR)烟气脱硝系统机理复杂，工况变化时呈现的不确定性、强扰动等特点，提出了一种基于互信息和PID神经网络的SCR烟气脱硝扰动补偿控制方法。利用PID前向神经网络的学习性能逼近被控对象的逆构成扰动观测器对系统进行反馈补偿，以达到超前消除系统扰动的目的。选取观测扰动和系统扰动的互信息为目标函数，采用改进的帝国竞争算法实现PID神经网络权值的优化调整。设计鲁棒PID控制器来进一步克服被控对象存在的不确定性。仿真实验表明，该方法具有突出的抗干扰能力和较好的鲁棒性，控制品质优于常规的PID控制。     

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。     

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.