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Application of adaptive neuro-fuzzy inference system and cuckoo optimization algorithm for analyzing

Reza TEIMOURI, Hamed SOHRABPOOR

《机械工程前沿（英文）》 2013年 第8卷 第4期   页码 429-442 doi: 10.1007/s11465-013-0277-3

摘要：

Electrochemical machining process (ECM) is increasing its importance due to some of the specific advantages which can be exploited during machining operation. The process offers several special privileges such as higher machining rate, better accuracy and control, and wider range of materials that can be machined. Contribution of too many predominate parameters in the process, makes its prediction and selection of optimal values really complex, especially while the process is programmized for machining of hard materials. In the present work in order to investigate effects of electrolyte concentration, electrolyte flow rate, applied voltage and feed rate on material removal rate (MRR) and surface roughness (SR) the adaptive neuro-fuzzy inference systems (ANFIS) have been used for creation predictive models based on experimental observations. Then the ANFIS 3D surfaces have been plotted for analyzing effects of process parameters on MRR and SR. Finally, the cuckoo optimization algorithm (COA) was used for selection solutions in which the process reaches maximum material removal rate and minimum surface roughness simultaneously. Results indicated that the ANFIS technique has superiority in modeling of MRR and SR with high prediction accuracy. Also, results obtained while applying of COA have been compared with those derived from confirmatory experiments which validate the applicability and suitability of the proposed techniques in enhancing the performance of ECM process.

关键词： electrochemical machining process (ECM)     modeling     adaptive neuro-fuzzy inference system (ANFIS)     optimization     cuckoo optimization algorithm (COA)    

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Application of a Novel Fuzzy Clustering Method Based on Chaos Immune Evolutionary Algorithm for Edge

《机械工程前沿（英文）》 2006年 第1卷 第1期   页码 85-89 doi: 10.1007/s11465-005-0023-6

摘要：

A novel fuzzy clustering method based on chaos immune evolutionary algorithm (CIEFCM) is presented to solve fuzzy edge detection problems in image processing. In CIEFCM, a tiny disturbance is added to a filial generation group using a chaos variable and the disturbance amplitude is adjusted step by step, which greatly improves the colony diversity of the immune evolution algorithm (IEA). The experimental results show that the method not only can correctly detect the fuzzy edge and exiguous edge but can evidently improve the searching efficiency of fuzzy clustering algorithm based on IEA.

关键词： disturbance amplitude     disturbance     diversity     generation     processing    

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Prediction of falling weight deflectometer parameters using hybrid model of genetic algorithm and adaptiveneuro-fuzzy inference system

《结构与土木工程前沿（英文）》   页码 812-826 doi: 10.1007/s11709-023-0940-7

摘要： A falling weight deflectometer is a testing device used in civil engineering to measure and evaluate the physical properties of pavements, such as the modulus of the subgrade reaction (Y1) and the elastic modulus of the slab (Y2), which are crucial for assessing the structural strength of pavements. In this study, we developed a novel hybrid artificial intelligence model, i.e., a genetic algorithm (GA)-optimized adaptive neuro-fuzzy inference system (ANFIS-GA), to predict Y1 and Y2 based on easily determined 13 parameters of rigid pavements. The performance of the novel ANFIS-GA model was compared to that of other benchmark models, namely logistic regression (LR) and radial basis function regression (RBFR) algorithms. These models were validated using standard statistical measures, namely, the coefficient of correlation (R), mean absolute error (MAE), and root mean square error (RMSE). The results indicated that the ANFIS-GA model was the best at predicting Y1 (R = 0.945) and Y2 (R = 0.887) compared to the LR and RBFR models. Therefore, the ANFIS-GA model can be used to accurately predict Y1 and Y2 based on easily measured parameters for the appropriate and rapid assessment of the quality and strength of pavements.

关键词： falling weight deflectometer     modulus of subgrade reaction     elastic modulus     metaheuristic algorithms    

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Application of fuzzy logic control algorithm as stator power controller of a grid-connected doubly-fed

Ridha CHEIKH, Arezki MENACER, Said DRID, Mourad TIAR

《能源前沿（英文）》 2013年 第7卷 第1期   页码 49-55 doi: 10.1007/s11708-012-0217-7

摘要： This paper discusses the power outputs control of a grid-connected doubly-fed induction generator (DFIG) for a wind power generation systems. The DFIG structure control has a six diode rectifier and a PWM IGBT converter in order to control the power outputs of the DFIG driven by wind turbine. So, to supply commercially the electrical power to the grid without any problems related to power quality, the active and reactive powers ( , ) at the stator side of the DFIG are strictly controlled at a required level, which, in this paper, is realized with an optimized fuzzy logic controller based on the grid flux oriented control, which gives an optimal operation of the DFIG in sub-synchronous region, and the control of the stator power flow with the possibility of keeping stator power factor at a unity.

关键词： doubly-fed induction generator (DFIG)     vector control     fuzzy logic controller     optimization     power factor unity     active and reactive power    

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Semi-active fuzzy control of Lali Cable-Stayed Bridge using MR dampers under seismic excitation

Sajad JAVADINASAB HORMOZABAD, Amir K. GHORBANI-TANHA

《结构与土木工程前沿（英文）》 2020年 第14卷 第3期   页码 706-721 doi: 10.1007/s11709-020-0612-9

摘要： Seismic control of cable-stayed bridges is of paramount importance due to their complex dynamic behavior, high flexibility, and low structural damping. In the present study, several semi-active Fuzzy Control Algorithms (FCAs) for vibration mitigation of Lali Cable-Stayed Bridge are devised. To demonstrate the efficiency of the algorithms, a comprehensive nonlinear 3-D model of the bridge is created using OpenSees. An efficient method for connecting MATLAB and OpenSees is devised for applying FCAs to the structural model of the bridge. Two innovative fuzzy rule-bases are introduced. A total of six different fuzzy rule-bases are utilized. The efficiency of the FCAs is evaluated in a comparative manner. The performance of fuzzy control systems is also compared with a sky-hook and a passive-on system. Moreover, the sensitivity of efficiency of control systems to the peak ground acceleration is evaluated qualitatively. In addition, the effect of time lag is also investigated. This study thoroughly examines the efficiency of the FCAs in different aspects. Therefore, the results can be regarded as a general guide to design semi-active fuzzy control systems for vibration mitigation of cable-stayed bridges.

关键词： semi-active control     Fuzzy Control Algorithm     cable-stayed bridge     MR damper     Lali Bridge    

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Intelligent hybrid power generation system using new hybrid fuzzy-neural for photovoltaic system and

Alireza REZVANI,Ali ESMAEILY,Hasan ETAATI,Mohammad MOHAMMADINODOUSHAN

《能源前沿（英文）》 2019年 第13卷 第1期   页码 131-148 doi: 10.1007/s11708-017-0446-x

摘要： Photovoltaic (PV) generation is growing increasingly fast as a renewable energy source. Nevertheless, the drawback of the PV system is intermittent because of depending on weather conditions. Therefore, the wind power can be considered to assist for a stable and reliable output from the PV generation system for loads and improve the dynamic performance of the whole generation system in the grid connected mode. In this paper, a novel topology of an intelligent hybrid generation system with PV and wind turbine is presented. In order to capture the maximum power, a hybrid fuzzy-neural maximum power point tracking (MPPT) method is applied in the PV system. The average tracking efficiency of the hybrid fuzzy-neural is incremented by approximately two percentage points in comparison with the conventional methods. The pitch angle of the wind turbine is controlled by radial basis function network-sliding mode (RBFNSM). Different conditions are represented in simulation results that compare the real power values with those of the presented methods. The obtained results verify the effectiveness and superiority of the proposed method which has the advantages of robustness, fast response and good performance. Detailed mathematical model and a control approach of a three-phase grid-connected intelligent hybrid system have been proposed using Matlab/Simulink.

关键词： photovoltaic     wind turbine     hybrid system     fuzzy logic controller     genetic algorithm     RBFNSM    

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Assessment of novel nature-inspired fuzzy models for predicting long contraction scouring and related

《结构与土木工程前沿（英文）》 2021年 第15卷 第3期   页码 665-681 doi: 10.1007/s11709-021-0713-0

摘要： The scouring phenomenon is one of the major problems experienced in hydraulic engineering. In this study, an adaptive neuro-fuzzy inference system is hybridized with several evolutionary approaches, including the ant colony optimization, genetic algorithm, teaching-learning-based optimization, biogeographical-based optimization, and invasive weed optimization for estimating the long contraction scour depth. The proposed hybrid models are built using non-dimensional information collected from previous studies. The proposed hybrid intelligent models are evaluated using several statistical performance metrics and graphical presentations. Besides, the uncertainty of models, variables, and data are inspected. Based on the achieved modeling results, adaptive neuro-fuzzy inference system–biogeographic based optimization (ANFIS-BBO) provides superior prediction accuracy compared to others, with a maximum correlation coefficient (Rtest = 0.923) and minimum root mean square error value (RMSEtest = 0.0193). Thus, the proposed ANFIS-BBO is a capable cost-effective method for predicting long contraction scouring, thus, contributing to the base knowledge of hydraulic structure sustainability.

关键词： long contraction scour     prediction     uncertainty     ANFIS model     meta-heuristic algorithm    

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一种改进的和声搜索算法及其在权重模糊产生式规则获取中的应用 Research Article

叶绍强1,周恺卿1,Azlan Mohd ZAIN2,王方岭1,Yusliza YUSOFF2

《信息与电子工程前沿（英文）》 2023年 第24卷 第11期   页码 1574-1590 doi: 10.1631/FITEE.2200334

摘要： 和声搜索算法(harmony search, HS)是一种随机元启发式算法，其灵感来自于音乐家的即兴创作过程。针对HS在求解中易陷入局部极值等不足，本文提出一种混合布谷鸟算子的改进的和声布谷鸟搜索算法(modified HS with a hybrid cuckoo search (CS) operator, HS-CS)增强全局搜索能力。该算法首先对HS音高扰动调整方法的随机性进行分析，根据和声库中解的质量生成自适应惯性权重，并重构微调带宽寻优，提升HS的寻优效率及精度。其次，引入CS算子扩大解空间的搜索范围和提高种群密度，从而能够在随机生成和声和更新阶段快速跳出局部极值。最后，构建动态参数调整机制以提高算法寻优的效率。通过证明3个定理揭示HS-CS是一种全局收敛的元启发式算法。在实验部分，选取12种经典的测试函数优化求解以验证HS-CS算法的性能。数值分析结果表明，HS-CS在处理高维函数优化问题上显著优于其他算法，表现出较强鲁棒性、高收敛速度以及收敛精度。为进一步验证算法在实际问题求解中的有效性，将HS-CS用于优化BP神经网络进行加权模糊产生式的规则抽取。仿真实验结果表明，HS-CS优化后的BP神经网络能够获得较高的规则分类精度。因此，从理论和应用方面都证明了HS-CS是行之有效的。

关键词： 和声搜索算法；布谷鸟搜索算法；全局收敛；函数优化；权重模糊产生式规则抽取    

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Intelligent diagnosis methods for plant machinery

Huaqing WANG, Peng CHEN, Shuming WANG,

《机械工程前沿（英文）》 2010年 第5卷 第1期   页码 118-124 doi: 10.1007/s11465-009-0084-z

摘要： This paper reports several intelligent diagnostic approaches based on artificial neural network and fuzzy algorithm for plant machinery, such as the diagnosis method using the wavelet transform, rough sets, and fuzzy neural network; the diagnosis method based on the sequential inference and fuzzy neural network; the diagnosis approach by the possibility theory and certainty factor model; and the diagnosis method on the basis of the adaptive filtering technique and fuzzy neural network. These intelligent diagnostic methods have been successfully applied to condition diagnosis in different types of practical plant machinery.

关键词： intelligent diagnosis     neural network     fuzzy algorithm     adaptive filtering     plant machinery    

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Fuzzy force control of constrained robot manipulators based on impedance model in an unknown environment

LIU Hongyi, WANG Fei, WANG Lei

《机械工程前沿（英文）》 2007年 第2卷 第2期   页码 168-174 doi: 10.1007/s11465-007-0028-4

摘要： To precisely implement the force control of robot manipulators in an unknown environment, a control strategy based on fuzzy prediction of the reference trajectory in the impedance model is developed. The force tracking experiments are executed in an open-architecture control system with different tracking velocities, different desired forces, different contact stiffnesses and different surface figurations. The corresponding force control results are compared and analyzed. The influences of unknown parameters of the environment on the contact force are analyzed based on experimental data, and the tunings of predictive scale factors are illustrated. The experimental results show that the desired trajectory in the impedance model is predicted exactly and rapidly in the cases that the contact surface is unknown, the contact stiffness changes, and the fuzzy force control algorithm has high adaptability to the unknown environment.

关键词： predictive     tracking     corresponding     stiffness     algorithm    

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模糊基函数神经网络在线跟踪自学习算法研究

许飞云,钟秉林,黄仁

《中国工程科学》 2007年 第9卷 第11期   页码 48-53

摘要：

提出了一种用于分类的模糊基函数（FBF）神经网络在线跟踪自学习算法，通过带有遗忘因子的样本均值和样本协方差矩阵，保存了原始样本所包含的类可能性分布信息，并在此基础上产生新增样本的目标输出用于训练FBF网络，以实现分类边界的在线跟踪；给出了带有遗忘因子的样本均值和样本协方差矩阵的递推算法，以克服传统方法需要保存大量以往训练样本带来的困难。所提出的方法用于旋转机械的故障识别，结果表明是可行的和有效的。

关键词： 模糊基函数     自学习     故障诊断    

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模式识别技术在泥浆浓度反演中的应用

李德军,吕艳华,王润田

《中国工程科学》 2007年 第9卷 第5期   页码 81-84

摘要：

泥浆在建筑工程中使用非常普遍，合理地控制泥浆的物理性能对于建筑工程施工及其质量控制非常 重要，通过声学方法可以有效地监测泥浆的体积浓度等物理参数。在通过声衰减和声速等介质的声学参数反演 泥浆浓度的过程中，数据拟合的好坏直接影响到反演的精确程度。通过模式识别技术，利用聚类算法，对数据 进行分类、归类处理，能有效的地提高反演的准确度。

关键词： 模式识别     最近邻法     聚类算法     泥浆浓度    

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基于强化模糊认知图实现数据与知识协作的氟化铝添加量决策方法 Article

岳伟超, 桂卫华, 陈晓方, 曾朝晖, 谢永芳

《工程（英文）》 2019年 第5卷 第6期   页码 1060-1076 doi: 10.1016/j.eng.2019.10.005

摘要：

在铝电解过程中，添加氟化铝能降低电解质的初晶温度，从而提高电流效率。氟化铝添加量的决策是一项复杂的知识型工作，需要考虑许多相关的因素，在实际生产中主要依赖于人工经验。由于工艺人员的主观性以及铝电解槽的复杂性，基于知识或者基于数据的决策方法难以保证添加的准确性。现有的决策方法难以囊括复杂的因果关系。本文针对氟化铝添加量的决策提出了一种基于强化模糊认知图的数据与知识协作策略。在这种方法中，改进的模糊k均值和模糊决策树用于提取模糊规则，其中提取的规则用于修正专家提出的初始框架。同时，采用状态转移优化算法（STA）获取强化模糊认知图的权重。将提出的方法与已有方法进行对比，结果表明，强化模糊认知的收敛速度快于基于Hebbian学习方法、粒子群优化方法以及遗传算法。不仅如此，基于所提方法氟化铝添加量的决策准确率高于其他方法。因此，针对氟化铝添加量的决策，本文提出的方法是有效的。

关键词： 氟化铝添加     模糊认知图     学习方法     状态转移优化方法     模糊决策树    

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Energy-aware fuzzy job-shop scheduling for engine remanufacturing at the multi-machine level

Jiali ZHAO, Shitong PENG, Tao LI, Shengping LV, Mengyun LI, Hongchao ZHANG

《机械工程前沿（英文）》 2019年 第14卷 第4期   页码 474-488 doi: 10.1007/s11465-019-0560-z

摘要： The rise of the engine remanufacturing industry has resulted in increased possibilities of energy conservation during the remanufacturing process, and scheduling could exert significant effects on the energy performance of manufacturing systems. However, only a few studies have specifically addressed energy-efficient scheduling for remanufacturing. Considering the uncertain processing time and routes and the operation characteristics of remanufacturing, we used the crankshaft as an illustrative case and built a fuzzy job-shop scheduling model to minimize the energy consumption during remanufacturing. An improved adaptive genetic algorithm was developed by using the hormone modulation mechanism to deal with the scheduling problem that simultaneously involves parallel machines, batch machines, and uncertain processing routes and time. The algorithm demonstrated superior performance in terms of optimal value, run time, and convergent generation in comparison with other algorithms. Computational results indicated that the optimal scheduling scheme is expected to generate 1.7 kW∙h of energy saving for the investigated problem size. In addition, the scheme could improve the energy efficiency of the crankshaft remanufacturing process by approximately 5%. This study provides a basis for production managers to improve the sustainability of remanufacturing through energy-aware scheduling.

关键词： remanufacturing scheduling     adaptive genetic algorithm     energy efficiency     sustainable remanufacturing     hormone modulation mechanism    

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不完备模糊信息系统

杨习贝,杨静宇,吴陈,傅凡

《中国工程科学》 2006年 第8卷 第7期   页码 47-53

摘要：

以不完备模糊信息系统为研究对象，建立了其中的模糊相容关系及模糊粗糙上、下近似集。在此基础上，探讨了论域上的模糊覆盖问题并提出了覆盖的3种运算形式；定义了2种新的模糊粗糙熵以讨论不完备模糊信息系统中的不确定性因素，证明了不确定因素的变化与度量强度之间的重要关系；建立了一种度量部分模糊知识依赖的新方法，获得了一些新的定理结果证明。

关键词： 不完备模糊信息系统     模糊相容关系     模糊粗糙集     模糊覆盖     模糊粗糙熵     模糊知识依赖    

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标题 作者 时间 类型 操作

Application of adaptive neuro-fuzzy inference system and cuckoo optimization algorithm for analyzing

Reza TEIMOURI, Hamed SOHRABPOOR

期刊论文

Application of a Novel Fuzzy Clustering Method Based on Chaos Immune Evolutionary Algorithm for Edge

期刊论文

Prediction of falling weight deflectometer parameters using hybrid model of genetic algorithm and adaptiveneuro-fuzzy inference system

期刊论文

Application of fuzzy logic control algorithm as stator power controller of a grid-connected doubly-fed

Ridha CHEIKH, Arezki MENACER, Said DRID, Mourad TIAR

期刊论文

Semi-active fuzzy control of Lali Cable-Stayed Bridge using MR dampers under seismic excitation

Sajad JAVADINASAB HORMOZABAD, Amir K. GHORBANI-TANHA

期刊论文

Intelligent hybrid power generation system using new hybrid fuzzy-neural for photovoltaic system and

Alireza REZVANI,Ali ESMAEILY,Hasan ETAATI,Mohammad MOHAMMADINODOUSHAN

期刊论文

Assessment of novel nature-inspired fuzzy models for predicting long contraction scouring and related

期刊论文

一种改进的和声搜索算法及其在权重模糊产生式规则获取中的应用

叶绍强1,周恺卿1,Azlan Mohd ZAIN2,王方岭1,Yusliza YUSOFF2

期刊论文

Intelligent diagnosis methods for plant machinery

Huaqing WANG, Peng CHEN, Shuming WANG,

期刊论文

Fuzzy force control of constrained robot manipulators based on impedance model in an unknown environment

LIU Hongyi, WANG Fei, WANG Lei

期刊论文

模糊基函数神经网络在线跟踪自学习算法研究

许飞云,钟秉林,黄仁

期刊论文

模式识别技术在泥浆浓度反演中的应用

李德军,吕艳华,王润田

期刊论文

基于强化模糊认知图实现数据与知识协作的氟化铝添加量决策方法

岳伟超, 桂卫华, 陈晓方, 曾朝晖, 谢永芳

期刊论文

Energy-aware fuzzy job-shop scheduling for engine remanufacturing at the multi-machine level

Jiali ZHAO, Shitong PENG, Tao LI, Shengping LV, Mengyun LI, Hongchao ZHANG

期刊论文

不完备模糊信息系统

杨习贝,杨静宇,吴陈,傅凡

期刊论文