Application of fuzzy analytic hierarchy process and neural network in power transformer risk assessment

In operation,risk arising from power transformer faults is of much uncertainty and complicacy.To timely and objectively control the risks,a transformer risk assessment method based on fuzzy analytic hierarchy process(FAHP) and artificial neural network(ANN) from the perspective of accuracy and quickness is proposed.An analytic hierarchy process model for the transformer risk assessment is built by analysis of the risk factors affecting the transformer risk level and the weight relation of each risk factor in transformer risk calculation is analyzed by application of fuzzy consistency judgment matrix;with utilization of adaptive ability and nonlinear mapping ability of the ANN,the risk factors with large weights are used as input of neutral network,and thus intelligent quantitative assessment of transformer risk is realized.The simulation result shows that the proposed method increases the speed and accuracy of the risk assessment and can provide feasible decision basis for the transformer risk management and maintenance decisions.

Using Extension Theory and Fuzzy Analytic Hierarchy Process on Heterogeneous Wireless Network Selection Algorithm

In order to solve the problem that determining decision factors weights is of subjectivity in heterogeneous wireless network selection algorithm, a network selection algorithm based on extension theory and fuzzy analytic hierarchy process （FAHP） is proposed in this paper. In addition, user and operator codetermine the optimal network using the proposed algorithm, which can give consideration to user and operator benefits. The fuzzy judgment matrix is coustructed by membership degree of decision factors which is calculated according to extension theory. The comprehensive weight of each decision factor is obtained using FAHP. Finally, the optimal network is selected through total property value ranldng of each candidate network under user preference and operator preference. The simulation results show that the proposed algorithm can select the optimal network efficiently and accurately, satisfy user preference, and implement load balance between networks.

Modeling of goethite iron precipitation process based on time-delay fuzzy gray cognitive network

The goethite iron precipitation process consists of several continuous reactors and involves a series of complex chemical reactions,such as oxidation reaction,hydrolysis reaction and neutralization reaction.It is hard to accurately establish a mathematical model of the process featured by strong nonlinearity,uncertainty and time-delay.A modeling method based on time-delay fuzzy gray cognitive network(T-FGCN)for the goethite iron precipitation process was proposed in this paper.On the basis of the process mechanism,experts’practical experience and historical data,the T-FGCN model of the goethite iron precipitation system was established and the weights were studied by using the nonlinear hebbian learning(NHL)algorithm with terminal constraints.By analyzing the system in uncertain environment of varying degrees,in the environment of high uncertainty,the T-FGCN can accurately simulate industrial systems with large time-delay and uncertainty and the simulated system can converge to steady state with zero gray scale or a small one.

Modeling of Energy Consumption and Effluent Quality Using Density Peaks-based Adaptive Fuzzy Neural Network

Modeling of energy consumption(EC) and effluent quality(EQ) are very essential problems that need to be solved for the multiobjective optimal control in the wastewater treatment process(WWTP). To address this issue, a density peaks-based adaptive fuzzy neural network(DP-AFNN) is proposed in this study. To obtain suitable fuzzy rules, a DP-based clustering method is applied to fit the cluster centers to process nonlinearity.The parameters of the extracted fuzzy rules are fine-tuned based on the improved Levenberg-Marquardt algorithm during the training process. Furthermore, the analysis of convergence is performed to guarantee the successful application of the DPAFNN. Finally, the proposed DP-AFNN is utilized to develop the models of EC and EQ in the WWTP. The experimental results show that the proposed DP-AFNN can achieve fast convergence speed and high prediction accuracy in comparison with some existing methods.

An Optimized Vertical Handoff Algorithm Based on Markov Process in Vehicle Heterogeneous Network

In order to solve the problem the existing vertical handoff algorithms of vehicle heterogeneous wireless network do not consider the diversification of network's status, an optimized vertical handoff algorithm based on markov process is proposed and discussed in this paper. This algorithm takes into account that the status transformation of available network will affect the quality of service(Qo S) of vehicle terminal's communication service. Firstly, Markov process is used to predict the transformation of wireless network's status after the decision via transition probability. Then the weights of evaluating parameters will be determined by fuzzy logic method. Finally, by comparing the total incomes of each wireless network, including handoff decision incomes, handoff execution incomes and communication service incomes after handoff, the optimal network to handoff will be selected. Simulation results show that: the algorithm proposed, compared to the existing algorithm, is able to receive a higher level of load balancing and effectively improves the average blocking rate, packet loss rate and ping-pang effect.

FUZZY NEURAL NETWORK FOR MACHINE PARTS RECOGNITION SYSTEM

The primary purpose is to develop a robust adaptive machine parts recognitionsystem. A fuzzy neural network classifier is proposed for machine parts classifier. It is anefficient modeling method. Through learning, it can approach a random nonlinear function. A fuzzyneural network classifier is presented based on fuzzy mapping model. It is used for machine partsclassification. The experimental system of machine parts classification is introduced. A robustleast square back-propagation (RLSBP) training algorithm which combines robust least square (RLS)with back-propagation (BP) algorithm is put forward. Simulation and experimental results show thatthe learning property of RLSBP is superior to BP.

Evaluation of Green Development Level of Electric Energy in Distribution Network Based on Multilevel Fuzzy Comprehensive Evaluation

At present,there are few studies on the comprehensive evaluation of green power grid development in China,and all aspects of green power grid need to be evaluated.Therefore,this paper studies the green development level of power distribution network.This paper proposes a multi-level fuzzy comprehensive evaluation method,which first needs to classify the influencing factors.Therefore,this paper constructs an indicator system for the evaluation of green development of power distribution network from three dimensions.In order to avoid the influence of subjective factors,this paper adopts the model combining analytic hierarchy process and entropy weight method to give weight to indexes.Finally,five typical regions are selected for empirical analysis.The results show that the model established in this paper can reflect the green development level of power distribution network in each region and put forward relevant improvement suggestions for each region.

基于ISM-ANP-Fuzzy算法的地铁车站火灾安全韧性评价体系

目的:为了减少地铁车站火灾事故的发生,需更深入地探究地铁车站火灾安全韧性的形成机理。方法:简要介绍了韧性及安全韧性的概念,建立了地铁车站火灾安全韧性模型。建立了基于ISM(解释结构模型)-ANP(网络分析法)-Fuzzy(模糊综合评价)算法的地铁车站火灾安全韧性评价体系,利用ISM法确定了各指标间相互影响关系,采用ANP计算得到各指标的权重,采用Fuzzy法对指标进行定量分析,以消除专家打分的主观性。最后以上海轨道交通线网的人民广场换乘站为案例,对该评价体系的适用性进行评估。结果及结论:该体系具有较强的适用性,可有效测量地铁火灾的安全韧性。提高抵御能力和适应能力的韧性等级,是提升地铁车站消防体系火灾应对能力的有效途径。提升火灾自动报警系统及喷灭火系统的质量,通过培训来提高车站安全管理人员的能力水平和专业程度,是提高地铁车站火灾安全韧性的有效措施。

基于模糊神经网络的电网消防预警算法

针对传统基于阈值判别方法的电网火灾预警系统预测精度低、抗干扰能力弱的问题,提出了一种基于模糊神经网络的电网消防预警算法。该算法利用神经网络学习大规模电网数据,使用模糊逻辑推理算法来提升预测结果的推理能力,并通过结合神经网络对大规模数据的学习能力和模糊逻辑算法的推理能力来分析电网线路参数,从而提升电网消防预警系统的精度和抗干扰能力。实验与仿真结果表明,所提出方法能显著提升电网火灾的预警精度,且使用模糊逻辑推理可以得到更符合实际情况的电网火灾预警结果。

基于组合赋权云模型的塔台管制系统运行安全评估

随着我国民航业的迅猛发展,空中交通流量不断攀升,机场塔台管制系统的运行安全面临严峻考验,因此,正确识别和评估风险成为有效提高塔台管制系统运行安全水平的重要前提。为精准且有效地完成对塔台管制系统运行的安全评估,构建了基于模糊决策实验室分析法(Decision-Making Trial and Evaluation Laboratory,DEMATEL)网络层次分析法(Analytic Network Process,ANP)熵权法云模型的塔台管制系统运行安全评估模型。首先,通过流程图法并结合人机环管的思想建立塔台管制系统运行安全评估指标体系,并采用模糊DEMATEL法确定评估指标间的相互影响关系,绘制评估指标之间的网络结构图;然后,通过ANP法确定各评估指标的主观权重,采用熵权法确定各评估指标的客观权重,并通过博弈论方法计算评估指标的综合权重;最后,将权重结果与评语层云模型结合使用对各级安全评估指标进行安全评估,通过MATLAB软件完成算例的仿真分析。结果表明:该塔台管制系统运行安全等级在一般和良好之间,仍需采取有效措施对某些风险因素进行控制;同时验证了该模型在塔台管制系统运行安全评估中具有可行性和准确性,对塔台管制系统安全评估具有指导意义。

海南省波浪能开发智能化适宜性分析模型研究

针对目前波浪能开发适宜性分析方法存在的问题及工作量大、成本高的特点,基于地理信息系统(Geographic information system, GIS)、改进的多准则决策方法(Multi-criteria decision making, MCDM)和人工神经网络(Artificial neural network, ANN)提出海南省波浪能开发智能化适宜性分析模型。对于该模型:首先,使用模糊层次分析(Fuzzy analytic hierarchy process, FAHP)法计算评价指标权重,降低专家主观偏差,更好地描述信息的不确定性;其次,提出灰色关联分析-折衷优化(Grey relation analysis-vlse kriterijumska optimizacijaⅠkompromisno resenje, GRA-VIKOR)法计算开发适宜性指数,解决评价过程中部分信息丢失、评价结果不准确的问题;最后依托反向传播(Back propagation, BP)神经网络进行模型训练并进行验证,实现海南省波浪能开发适宜性分析的智能化,不仅提高工作效率,而且降低计算成本。通过模型得到海南省波浪能可开发区域和开发适宜性等级,为海南省波浪能选址决策奠定基础,同时可填补海南省波浪能开发适宜性分析在智能化领域的空白。

考虑时间不确定的再制造工艺方案决策方法

针对退役零件失效状态复杂及设备能力各异,导致再制造工艺方案决策困难的问题,提出一种考虑时间不确定的再制造工艺方案决策方法。分析退役零件再制造工艺过程及其时间的不确定性,提出考虑失效状态的再制造工艺时间改进模糊神经网络预测方法;构建以时间、成本和能耗为优化目标的再制造工艺方案决策模型,采用遗传-粒子群优化算法快速求解;以退役轧辊再制造工艺方案决策过程为例,验证该方法的有效性和实用性。

基于FAHP的大学生网络信息安全素养评价体系研究

网络信息安全事关国家安全,大学生群体既是网络信息应用重要的参与主体,又是网络信息安全事故易感群体。提升大学生网络安全素养,对于大学生强化网络信息安全认知、提高网络信息安全防范应对能力、降低网络信息安全事故发生率具有重要支撑作用。基于大学生网络信息安全相关理论,建立了一个涵盖4个维度17个子指标因素的大学生网络信息安全素养评价指标体系,运用模糊层次分析法构建模糊判断矩阵,计算出各指标权重,构建了大学生网络信息安全素养评价模型。结果表明,密码信息保护意识、信息隐私保护知识、信息真伪鉴别能力、负面信息抵制态度为大学生网络信息安全素养的关键影响因素。在此基础上,从国家、社会、高校、个人等不同层面,提出了提升大学生网络安全素养的对策建议。

基于DANP—模糊综合评价的危化品救援队伍应急能力评估

为量化评价我国危化品应急救援队伍能力,明确应急救援队伍建设中存在的不足,通过能力素质冰山模型研究影响因素,从应急准备、应急响应和总结评估3个方面建立评估指标体系,结合集成决策实验室分析法(DEMATEL)与网络层次分析法(ANP)确定指标权重,并和DEMATEL法的权重结果进行对比,运用模糊综合评价法建立全国危化品救援队伍应急能力评价模型。结果表明:应急准备能力评价为“较弱”,应急响应能力和总结评估能力评价为“一般”,队伍总体应急能力评价为“一般”,两种权重的评价结果一致。

鲁南地区流体监测台网优化与监测效能评估

结合鲁南地区地质构造背景,通过自主研发的高精度水位水温实时监测仪器,对地下流体监测站点软硬件设施进行升级改造,建设鲁南地区地下流体数字化监测台网。同时,构建基于层次分析法的鲁南地区地下流体监测效能评价体系,利用模糊综合评价法对各观测站点进行量化的效能评估,为进一步提升该地区地下流体监测台网监测能力提供参考意见。

基于Fuzzy ARTMAP神经网络的高分辨率图象土地覆盖分类及其评价

主要讨论了基于 Fuzzy ARTMAP神经网络的高分辨率遥感图象土地覆盖分类方法及其实践 .首先介绍了 Fuzzy ARTMAP神经网络的原理 ,然后用 SPOT XS图象试验数据进行土地覆盖分类 .分类结果与传统的最大似然监督分类 (ML C)、反馈式 (Back Propagation,BP)神经网络的分类结果进行了比较 .通过抽取 5 0 0个样点对 3种分类结果进行精度评价表明 ,Fuzzy ARTMAP神经网络相对其他两种方法 ,分类精度均有不同程度的改善 ,具有更好的分类结果 ,总分类精度比 ML C和 BP算法分别提高 17.4 1%、7.32 % .最后 ,对不同分类方法对于土地覆盖分类结果的影响进行了评价和分析 .试验表明 ,Fuzzy

基于ANP-Fuzzy方法的物流服务供应链流程对接绩效评价

分析了物流服务供应链协同运作的特点,从流程对接前、中、后3个时间段出发,建立了物流服务供应链协同运作流程对接评价指标体系.在评价方法上,综合考虑ANP和模糊评价法的优点,采用ANP方法来确定各指标的权重,用模糊评价法对各指标原始得分进行评价,建立ANP-Fuzzy综合绩效评价模型,并进行了算例分析.结果表明,该方法可以定量地评价物流服务供应链流程对接绩效,具有一定的推广价值.

基于ANP-Fuzzy的食品安全监管绩效评价研究——以山东省为例

评估政府食品安全监管绩效,对于助推政府加强食品安全的全方位监管,提高政府食品安全监管效率,改善食品安全治理具有重要意义。基于多环节监管视角,通过对国内外食品安全监管绩效评价的经典文献梳理,构建食品安全监管绩效评价指标体系,并借助网络层次-模糊综合评价模型(ANP-Fuzzy)实证分析了山东省食品安全监管绩效状况及其症结。

社交网络用户属性的隐私度量与决策分析方法

提出一种基于模糊层次分析和逼近理想解排序法的隐私决策优化方法,为用户在社交网络场景中实施隐私决策时提供可靠的隐私建议。首先,将社交属性的敏感度、独特性效用和普遍性效用作为价值评价指标,给出相应的量化方法,评价指标的权重计算过程是根据评价指标重要性的评价规则,采用模糊层次分析法构造模糊判断矩阵。然后,采用逼近理想解排序法对社交属性进行综合评估分析和排序,通过实验分析比较3种评价规则的个性化评估结果,实验结果表明该方法在满足个性化隐私需求的前提下能指导用户优化个人信息配置。

基于ANP-Fuzzy的MPL培训中威胁与差错管理能力评价

为定量考核飞行训练机构多人制机组驾驶员执照(MPL)训练效果,本文从教学环节入手,构建了MPL培训中威胁与差错管理能力的评价指标体系,并采用网络层次分析与模糊综合评价相结合的方法(ANP-Fuzzy方法)对飞行训练机构C的威胁与差错管理能力进行了评价,评价结果符合C机构实际。最后,就提升C机构MPL训练效果提出了相应建议。