Deep Structure Optimization for Incremental Hierarchical Fuzzy Systems Using Improved Differential Evolution Algorithm

The optimization of the rule base of a fuzzy logic system (FLS) based on evolutionary algorithm has achievednotable results. However, due to the diversity of the deep structure in the hierarchical fuzzy system (HFS) and thecorrelation of each sub fuzzy system, the uncertainty of the HFS’s deep structure increases. For the HFS, a largenumber of studies mainly use fixed structures, which cannot be selected automatically. To solve this problem, thispaper proposes a novel approach for constructing the incremental HFS. During system design, the deep structureand the rule base of the HFS are encoded separately. Subsequently, the deep structure is adaptively mutated basedon the fitness value, so as to realize the diversity of deep structures while ensuring reasonable competition amongthe structures. Finally, the differential evolution (DE) is used to optimize the deep structure of HFS and theparameters of antecedent and consequent simultaneously. The simulation results confirm the effectiveness of themodel. Specifically, the root mean square errors in the Laser dataset and Friedman dataset are 0.0395 and 0.0725,respectively with rule counts of rules is 8 and 12, respectively.When compared to alternative methods, the resultsindicate that the proposed method offers improvements in accuracy and rule counts.

Hierarchical Privacy Protection Model in Advanced Metering Infrastructure Based on Cloud and Fog Assistance

The Advanced Metering Infrastructure(AMI),as a crucial subsystem in the smart grid,is responsible for measuring user electricity consumption and plays a vital role in communication between providers and consumers.However,with the advancement of information and communication technology,new security and privacy challenges have emerged for AMI.To address these challenges and enhance the security and privacy of user data in the smart grid,a Hierarchical Privacy Protection Model in Advanced Metering Infrastructure based on Cloud and Fog Assistance(HPPM-AMICFA)is proposed in this paper.The proposed model integrates cloud and fog computing with hierarchical threshold encryption,offering a flexible and efficient privacy protection solution that significantly enhances data security in the smart grid.The methodology involves setting user protection levels by processing missing data and utilizing fuzzy comprehensive analysis to evaluate user importance,thereby assigning appropriate protection levels.Furthermore,a hierarchical threshold encryption algorithm is developed to provide differentiated protection strategies for fog nodes based on user IDs,ensuring secure aggregation and encryption of user data.Experimental results demonstrate that HPPM-AMICFA effectively resists various attack strategies while minimizing time costs,thereby safeguarding user data in the smart grid.

Hierarchical hesitant fuzzy K-means clustering algorithm

Due to the limitation and hesitation in one＇s knowledge, the membership degree of an element to a given set usually has a few different values, in which the conventional fuzzy sets are invalid. Hesitant fuzzy sets are a powerful tool to treat this case. The present paper focuses on investigating the clustering technique for hesitant fuzzy sets based on the K-means clustering algorithm which takes the results of hierarchical clustering as the initial clusters. Finally, two examples demonstrate the validity of our algorithm.

Intuitionistic fuzzy hierarchical clustering algorithms

Intuitionistic fuzzy set （IFS） is a set of 2-tuple arguments, each of which is characterized by a membership degree and a nonmembership degree. The generalized form of IFS is interval-valued intuitionistic fuzzy set （IVIFS）, whose components are intervals rather than exact numbers. IFSs and IVIFSs have been found to be very useful to describe vagueness and uncertainty. However, it seems that little attention has been focused on the clustering analysis of IFSs and IVIFSs. An intuitionistic fuzzy hierarchical algorithm is introduced for clustering IFSs, which is based on the traditional hierarchical clustering procedure, the intuitionistic fuzzy aggregation operator, and the basic distance measures between IFSs： the Hamming distance, normalized Hamming, weighted Hamming, the Euclidean distance, the normalized Euclidean distance, and the weighted Euclidean distance. Subsequently, the algorithm is extended for clustering IVIFSs. Finally the algorithm and its extended form are applied to the classifications of building materials and enterprises respectively.

A Fuzzy Logic Based Supervisory Hierarchical Control Scheme for Real Time Pressure Control

This paper describes a supervisory hierarchical fuzzy controller （SHFC） for regulating pressure in a real-time pilot pressure control system. The input scaling factor tuning of a direct expert controller is made using the error and process input parameters in a closed loop system in order to obtain better controller performance for set-point change and load disturbances. This on-line tuning method reduces operator involvement and enhances the controller performance to a wide operating range. The hierarchical control scheme consists of an intelligent upper level supervisory fuzzy controller and a lower level direct fuzzy controller. The upper level controller provides a mechanism to the main goal of the system and the lower level controller delivers the solutions to a particular situation. The control algorithm for the proposed scheme has been developed and tested using an ARM7 microcontroller-based embedded target board for a nonlinear pressure process having dead time. To demonstrate the effectiveness, the results of the proposed hierarchical controller, fuzzy controller and conventional proportional-integral （PI） controller are analyzed. The results prove that the SHFC performance is better in terms of stability and robustness than the conventional control methods.

Multi-Topology Hierarchical Collaborative Hybrid Particle Swarm Optimization Algorithm for WSN

Wireless sensor networks(WSN)are widely used in many situations,but the disordered and random deployment mode will waste a lot of sensor resources.This paper proposes a multi-topology hierarchical collaborative particle swarm optimization(MHCHPSO)to optimize sensor deployment location and improve the coverage of WSN.MHCHPSO divides the population into three types topology:diversity topology for global exploration,fast convergence topology for local development,and collaboration topology for exploration and development.All topologies are optimized in parallel to overcome the precocious convergence of PSO.This paper compares with various heuristic algorithms at CEC 2013,CEC 2015,and CEC 2017.The experimental results show that MHCHPSO outperforms the comparison algorithms.In addition,MHCHPSO is applied to the WSN localization optimization,and the experimental results confirm the optimization ability of MHCHPSO in practical engineering problems.

Location of Electric Vehicle Charging Station Based on Spatial Clustering and Multi-hierarchical Fuzzy Evaluation

For the charging station construction of electric vehicle,location selecting is a key issue.There are two problems in location selection of the electric vehicle charging station.One is determining the location of charging station;the other is evaluating the location of charging station.To determine the charging station location,an spatial clustering algorithm is proposed and programmed.The example simulation shows the effectiveness of the spatial clustering algorithm.To evaluate the charging station location,a multi-hierarchical fuzzy method is proposed.Based on the location factors of electric vehicle charging station,the hierarchical evaluation structure of electric vehicle charging station location is constructed,including three levels,4first-class factors and 14second-class factors.The fuzzy multi-hierarchical evaluation model and algorithm are built.The analysis results show that the multi-hierarchical fuzzy method can reasonably complete the electric vehicle charging station location evaluation.

Advanced Hierarchical Fuzzy Classification Model Adopting Symbiosis Based DNA-ABC Optimization Algorithm

This paper offers a symbiosis based hybrid modified DNA-ABC optimization algorithm which combines modified DNA concepts and artificial bee colony (ABC) algorithm to aid hierarchical fuzzy classification. According to literature, the ABC algorithm is traditionally applied to constrained and unconstrained problems, but is combined with modified DNA concepts and implemented for fuzzy classification in this present research. Moreover, from the best of our knowledge, previous research on the ABC algorithm has not combined it with DNA computing for hierarchical fuzzy classification to explore the merits of cooperative coevolution. Therefore, this paper is the first to apply the mechanism of symbiosis to create a hybrid modified DNA-ABC algorithm for hierarchical fuzzy classification applications. In this study, the partition number and the shape of the membership function are extracted by the symbiosis based hybrid modified DNA-ABC optimization algorithm, which provides both sufficient global exploration and also adequate local exploitation for hierarchical fuzzy classification. The proposed optimization algorithm is applied on five benchmark University of Irvine (UCI) data sets, and the results prove the efficiency of the algorithm.

A special hierarchical fuzzy neural-networks based reinforcement learning for multi-variables system

Proposes a reinforcement learning scheme based on a special Hierarchical Fuzzy Neural-Networks (HFNN)for solving complicated learning tasks in a continuous multi-variables environment. The output of the previous layer in the HFNN is no longer used as if-part of the next layer, but used only in then-part. Thus it can deal with the difficulty when the output of the previous layer is meaningless or its meaning is uncertain. The proposed HFNN has a minimal number of fuzzy rules and can successfully solve the problem of rules combination explosion and decrease the quantity of computation and memory requirement. In the learning process, two HFNN with the same structure perform fuzzy action composition and evaluation function approximation simultaneously where the parameters of neural-networks are tuned and updated on line by using gradient descent algorithm. The reinforcement learning method is proved to be correct and feasible by simulation of a double inverted pendulum system.

A Novel Real-Time Fault Diagnostic System for Steam Turbine Generator Set by Using Strata Hierarchical Artificial Neural Network

The real-time fault diagnosis system is very great important for steam turbine generator set due to a serious fault results in a reduced amount of electricity supply in power plant. A novel real-time fault diagnosis system is proposed by using strata hierarchical fuzzy CMAC neural network. A framework of the fault diagnosis system is described. Hierarchical fault diagnostic structure is discussed in detail. The model of a novel fault diagnosis system by using fuzzy CMAC are built and analyzed. A case of the diagnosis is simulated. The results show that the real-time fault diagnostic system is of high accuracy, quick convergence, and high noise rejection. It is also found that this model is feasible in real-time fault diagnosis.

Machine Learning for Data Fusion:A Fuzzy AHP Approach for Open Issues

Data fusion generates fused data by combining multiple sources,resulting in information that is more consistent,accurate,and useful than any individual source and more reliable and consistent than the raw original data,which are often imperfect,inconsistent,complex,and uncertain.Traditional data fusion methods like probabilistic fusion,set-based fusion,and evidential belief reasoning fusion methods are computationally complex and require accurate classification and proper handling of raw data.Data fusion is the process of integrating multiple data sources.Data filtering means examining a dataset to exclude,rearrange,or apportion data according to the criteria.Different sensors generate a large amount of data,requiring the development of machine learning(ML)algorithms to overcome the challenges of traditional methods.The advancement in hardware acceleration and the abundance of data from various sensors have led to the development of machine learning(ML)algorithms,expected to address the limitations of traditional methods.However,many open issues still exist as machine learning algorithms are used for data fusion.From the literature,nine issues have been identified irrespective of any application.The decision-makers should pay attention to these issues as data fusion becomes more applicable and successful.A fuzzy analytical hierarchical process(FAHP)enables us to handle these issues.It helps to get the weights for each corresponding issue and rank issues based on these calculated weights.The most significant issue identified is the lack of deep learning models used for data fusion that improve accuracy and learning quality weighted 0.141.The least significant one is the cross-domain multimodal data fusion weighted 0.076 because the whole semantic knowledge for multimodal data cannot be captured.

虚拟大学颠覆性创新:一个量化评估框架的构建

针对虚拟大学是否具有颠覆性创新潜质这一问题,目前学术界虽然存在截然不同的两种观点,但所采纳研究方法却较为一致,即均以理论思辨方法展开探讨,鲜见针对虚拟大学案例展开量化评估。究其原因,在于目前学术界尚缺乏可用于虚拟大学颠覆性创新的评估框架。据此,尝试设计了一套评估框架,该框架首先基于扎根理论分析法、借助NVivo软件编码建构了六大评估维度,包括教学服务、使用相对成本、开放性、组织独立性、大学市场运作机制及质量;继而,运用层次分析法和模糊综合评价法,针对六大维度所包含各项特征指标予以量化赋分以确定各部分权重。将所构建框架应用于四所有代表性的虚拟大学(学院),评估结果较为准确地反映了四所虚拟大学(学院)的发展现状和办学水平。所构建这一评估框架,无论对于志在争夺高等教育市场份额的虚拟大学,还是对于决意维系和增强自身竞争力并顺应教育数字化改革趋势的传统大学,均具有较强的理论和实践参考价值。

基于改进DEMATEL-ISM的钢桁梁桥施工风险影响因素研究

为提高钢桁梁桥施工过程中的安全管理,对钢桁梁桥施工中的风险因素进行分析,提出了一种基于改进DEMATEL-ISM的评价方法,首先引入三角模糊数对DEMATEL法中的直接影响矩阵进行改进,从而去模糊化以减少专家评价的主观性,再通过DEMATEL法分析各影响因素的影响度、被影响度、中心度与原因度并加以排序,对得到的主要影响因素进行初步分析,利用ISM法对筛选出的影响因素进行层次划分,得到直接层影响因素、过渡层影响因素与本质影响因素,并针对分析得到的5个本质影响因素提出深入的预防预警措施,可为钢桁梁桥施工方向的安全管理提供针对性措施,为事故预防提供一定的参考依据。

一种分层模糊Petri网风险评估方法

针对传统模糊Petri网在对不确定环境下的专家系统的知识表示与推理时无法兼顾不确定知识的模糊性与随机性、在复杂的故障情况下故障的因果关系表达不清晰、定量推理计算时缺乏层次性、不能局部求解的问题,构建一种基于云模型的分层模糊Petri网以加强模糊Petri网的知识表示能力和提高推理过程的计算效率。利用专家知识和Petri网层次分解原则将系统故障模式和故障原因之间的因果关系进行建模,使故障建模更具结构性,计算更加灵活;应用云模型处理知识的模糊性和不确定性;通过合理考虑局部权重和全局权重,结合Petri网层次分解原则和云聚合算子给出相应的推理算法。实例验证表明,所提方法能够有效对系统进行风险评估,且在知识表示和推理方面优于其他方法。

海战场环境下反舰导弹效能评估方法研究

对海打击是海上编队最为重要的作战样式之一,开展武器装备作战效能评估方法研究,科学评估其作战能力,是装备列装定型的必要条件。基于作战能力评估的复杂性,在系统分析以往效能评估方法和步骤基础上,建立了武器装备不同类型指标的归一化模型和关键作战能力评估模型,可为靶场开展作战效能评估提供重要理论参考,为部队作战使用提供支撑和帮助。

基于风电分时电价的虚拟电厂参与清洁供暖运营优化方法

随着“碳达峰、碳中和”目标的提出,迅速发展的风电由于其随机性与波动性,面临突出的风电消纳问题。蓄热式电采暖作为中国北方地区主要供暖设备,虚拟电厂作为需求侧资源的主要聚合技术手段,聚合蓄热式电采暖的虚拟电厂可为消纳风电、提高风电利用率提供解决途径。对此,提出一种基于风电功率的分时电价划分方法,实现虚拟电厂聚合蓄热式电采暖参与基于分时电价的清洁供暖交易优化运营。首先,阐述虚拟电厂聚合蓄热式电采暖用户参与风电供暖的交易模式;其次,考虑热惯性对蓄热式电采暖和房屋进行精细化建模,提出基于层次凝聚聚类算法的分时电价方法,建立基于Weber-Fechner定律的负荷模糊响应模型,并构建多方主体综合收益最大、弃风量最小和负荷波动最小的虚拟电厂多目标运营优化模型;最后,通过算例分析风电消纳效果和虚拟电厂收益,验证该方法能够有效促进风电消纳、提高多方主体积极性,并具有一定的规模经济性,以期为缓解弃风问题提供参考。

基于ISM-fuzzy MICMAC方法的PPP项目关键风险层级关系识别

PPP项目失败往往并非由单一风险因素引起,准确分析导致PPP项目失败的风险因素间的相互影响关系至关重要。采用多种风险识别方法,识别出20项易导致PPP项目失败的关键风险因素;利用ISM-fuzzy和MICMAC方法构建关键风险因素相互影响的递阶层级结构,并结合各关键风险因素的依赖性和驱动力对风险因素进行分类,揭示出PPP项目的失败机理;最后,分析了各类关键风险因素的关联影响关系并提出了相应的风险管理建议。研究结果有助于PPP项目利益相关者识别项目执行过程中的关键风险因素和风险因素间的层级关系,尽早采取风险防范措施以降低项目失败的可能性,同时也可作为PPP项目风险管理和决策的有效辅助方法。

膨胀型透明防火涂料耐老化性模糊综合评价分析

膨胀型透明防火涂料因兼具良好的装饰性和阻燃性已成为文物古建筑防火保护的重要手段,但服役过程中面临的老化失效限制了其工业化的应用。为科学合理的评价透明防火涂料的耐老化性,首先,依据涂层人工加速老化试验结果,建立对象集、因素集和评语集,确定单因素评定矩阵;其次,采用层次分析法得到各指标间的权重关系,构建涂层老化指数公式,计算各对象老化性能的模糊综合评价值和涂层老化指数;最后,利用老化后涂层所表现的宏观性能验证模糊综合评价值和老化指数公式的合理性和有效性。结果表明:所评价的4种透明防火涂料的模糊综合评价值、老化指数与其宏观性能的劣化程度表现一致。

基于模糊控制的锂电池组分层均衡研究

传统的电感式均衡电路中要实现电池组均衡,能量只能通过相邻电池慢慢传递而不能在任意电池间转移。这种均衡方式效率低,均衡路径、时间长。为此,提出一种Buck-Boost电路与反激式电路相结合的新型分层拓扑。组内均衡采用双向Buck-Boost电路,使能量仅在相邻两节电池之间转移,因而均衡路径短。组间均衡采用双向反激式电路,通过控制原副边绕组开关管状态,使能量在模组与电池组间相互转移,从而达到电池组均衡。在均衡过程中加入模糊控制策略,动态调节开关管的占空比,以输出合适的均衡电流、提高均衡效率。搭建Matlab仿真模型进行分析。仿真结果表明,所提出的均衡电路相较于传统的Buck-Boost电路在静置和充、放电状态下均衡时间分别缩短约18%、17%和20%。均衡效率的提高对未来电动汽车等多锂电池串联设备的发展有着重要意义。