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期刊论文 16

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关键词

ANFIS 1

GA-ANFIS模型 1

不确定性 1

分层自适应神经模糊推理系统控制器；晶闸管控制串联电容器补偿技术；自动发电控制（AGC）；多目标粒子群优化算法；电力系统动态稳定性；相互联系的多源电力系统 1

地下开采 1

并行遗传算法 1

开采地面沉陷 1

技术经济参数 1

智能 1

桁架 1

自适应 1

自适应模糊神经网络 1

自适应神经模糊推理系统 1

评价指标 1

边坡稳定性评价 1

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基于ANFIS系统的基结构自适应生成

李旲,胡云昌,曹宏铎

《中国工程科学》 2004年 第6卷 第10期   页码 24-27

摘要：

以形成基结构智能自动生成系统为目标，以并行自适应神经-模糊推理系统（ANFIS）为工具，建立了具有桁架拓扑结构智能自动生成功能的并行ANFIS系统，并将形态化拓扑结构进行抽象数字提取，作为系统的输入输出数据最后的计算结果表明，这里使用的基于ANFIS系统的桁架结构智能自动生成方法是有效的，能够按照用户要求生成合理的桁架基结构拓扑形式。

关键词： 桁架     ANFIS     智能     自适应    

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基于GA-ANFIS的边坡稳定性评价

林咸志,薛涛,余鹏,陈青

《中国工程科学》 2011年 第13卷 第3期   页码 77-81

摘要： 结果表明，GA-ANFIS评判模型结果与现场监测情况吻合，从而使其成为边坡稳定性评价的一种有效方法。

关键词： GA-ANFIS模型     评价指标     不确定性     边坡稳定性评价    

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Reactive power compensation of an isolated hybrid power system with load interaction using ANFIS tuned

Nitin SAXENA,Ashwani KUMAR

《能源前沿（英文）》 2014年 第8卷 第2期   页码 261-268 doi: 10.1007/s11708-014-0298-6

摘要： This paper presents an adaptive neuro fuzzy interference system (ANFIS) based approach to tune the parameters of the static synchronous compensator (STATCOM) with frequent disturbances in load model and power input of a wind-diesel based isolated hybrid power system (IHPS). In literature, proportional integral (PI) based controller constants are optimized for voltage stability in hybrid systems due to the interaction of load disturbances and input power disturbances. These conventional controlling techniques use the integral square error (ISE) criterion with an open loop load model. An ANFIS tuned constants of a STATCOM controller for controlling the reactive power requirement to stabilize the voltage variation is proposed in the paper. Moreover, the interaction between the load and the isolated power system is developed in terms of closed loop load interaction with the system. Furthermore, a comparison of transient responses of IHPS is also presented when the system has only the STATCOM and the static compensation requirement of the induction generator is fulfilled by the fixed capacitor, dynamic compensation requirement, meanwhile, is fulfilled by STATCOM. The model is tested for a 1% step increase in reactive power load demand at = 0 s and then a sudden change of 3% from the 1% at = 0.01 s for a 1% step increase in power input at variable wind speed model.

关键词： isolated wind-diesel power system     adaptive neuro fuzzy interference system (ANFIS)     integral square error (ISE) criterion     load interaction    

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Enhancement of grid-connected photovoltaic system using ANFIS-GA under different circumstances

Saeed VAFAEI,Alireza REZVANI,Majid GANDOMKAR,Maziar IZADBAKHSH

《能源前沿（英文）》 2015年 第9卷 第3期   页码 322-334 doi: 10.1007/s11708-015-0362-x

摘要： In recent years, many different techniques are applied in order to draw maximum power from photovoltaic (PV) modules for changing solar irradiance and temperature conditions. Generally, the output power generation of the PV system depends on the intermittent solar insolation, cell temperature, efficiency of the PV panel and its output voltage level. Consequently, it is essential to track the generated power of the PV system and utilize the collected solar energy optimally. The aim of this paper is to simulate and control a grid-connected PV source by using an adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA) controller. The data are optimized by GA and then, these optimum values are used in network training. The simulation results indicate that the ANFIS-GA controller can meet the need of load easily with less fluctuation around the maximum power point (MPP) and can increase the convergence speed to achieve the MPP rather than the conventional method. Moreover, to control both line voltage and current, a grid side P/Q controller has been applied. A dynamic modeling, control and simulation study of the PV system is performed with the Matlab/Simulink program.

关键词： photovoltaic system     maximum power point (MPP)     adaptive neuro-fuzzy inference system (ANFIS)     genetic algorithm (GA)    

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Novel hybrid models of ANFIS and metaheuristic optimizations (SCE and ABC) for prediction of compressive

Dung Quang VU; Fazal E. JALAL; Mudassir IQBAL; Dam Duc NGUYEN; Duong Kien TRONG; Indra PRAKASH; Binh Thai PHAM

《结构与土木工程前沿（英文）》 2022年 第16卷 第8期   页码 1003-1016 doi: 10.1007/s11709-022-0846-9

摘要： In this study, we developed novel hybrid models namely Adaptive Neuro Fuzzy Inference System (ANFIS) optimized by Shuffled Complex Evolution (SCE) on the one hand and ANFIS with Artificial Bee Colony (ABC) on the other hand. These were used to predict compressive strength (Cs) of concrete relating to thirteen concrete-strength affecting parameters which are easy to determine in the laboratory. Field and laboratory tests data of 108 structural elements of 18 concrete bridges of the Ha Long-Van Don Expressway, Vietnam were considered. The dataset was randomly divided into a 70:30 ratio, for training (70%) and testing (30%) of the hybrid models. Performance of the developed fuzzy metaheuristic models was evaluated using standard statistical metrics: Correlation Coefficient (R), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results showed that both of the novel models depict close agreement between experimental and predicted results. However, the ANFIS-ABC model reflected better convergence of the results and better performance compared to that of ANFIS-SCE in the prediction of the concrete Cs. Thus, the ANFIS-ABC model can be used for the quick and accurate estimation of compressive strength of concrete based on easily determined parameters for the design of civil engineering structures including bridges.

关键词： shuffled complex evolution     artificial bee colony     ANFIS     concrete     compressive strength     Vietnam    

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收回说明：使用基于多目标PSO的分层ANFIS控制器-TCSC增强互连多电源系统的动态稳定性 Retraction Note

null

《信息与电子工程前沿（英文）》 2019年 第20卷 第5期 doi: 10.1631/FITEE.19r0001

摘要： None

关键词： None    

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Predication of discharge coefficient of cylindrical weir-gate using adaptive neuro fuzzy inference systems (ANFIS

Abbas PARSAIE,Amir Hamzeh HAGHIABI,Mojtaba SANEIE,Hasan TORABI

《结构与土木工程前沿（英文）》 2017年 第11卷 第1期   页码 111-122 doi: 10.1007/s11709-016-0354-x

摘要： Settlement of sediments behind weirs and accumulation of materials floating on water behind gates decreases the performance of these structures. Weir-gate is a combination of weir and gate structures which solves them Infirmities. Proposing a circular shape for crest of weirs to improve their performance, investigators have proposed cylindrical shape to improve the performance of weir-gate structure and call it cylindrical weir-gate. In this research, discharge coefficient of weir-gate was predicated using adaptive neuro fuzzy inference systems (ANFIS). To compare the performance of ANFIS with other types of soft computing techniques, multilayer perceptron neural network (MLP) was prepared as well. Results of MLP and ANFIS showed that both models have high ability for modeling and predicting discharge coefficient; however, ANFIS is a bit more accurate. The sensitivity analysis of MLP and ANFIS showed that Froude number of flow at upstream of weir and ratio of gate opening height to the diameter of weir are the most effective parameters on discharge coefficient.

关键词： weir-gate     soft computing     crest geometry     circular crest weir     cylindrical shape    

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Key uncertainty events impacting on the completion time of highway construction projects

Alireza MOGHAYEDI, Abimbola WINDAPO

《工程管理前沿（英文）》 2019年 第6卷 第2期   页码 275-298 doi: 10.1007/s42524-019-0022-7

摘要： This paper examines the uncertainty events encountered in the process of constructing highways, and evaluates their impact on construction time, on highway projects in South Africa. The rationale for this examination stems from the view held by scholars that the construction of highways is a complex process, taking place in changing environments and often beset by uncertainties; and that there is a lack of appropriate evaluation of these uncertainty events occurring during the construction process. The research made use of a review of extant literature in the area of uncertainty management, and modeling in infrastructure projects, to guide the direction of the study. The inquiry process consisted of brainstorming by highway experts and interviewing them to identify the uncertainty factors that impact construction time. An uncertainty matrix for South African highway projects was developed, using a quantitative model and descriptive statistics. It emerged from the study that the uncertainty events affecting the construction time of highway projects are distributed across economic, environmental, financial, legal, political, social and technical factors. Also, it was found that each factor might account for several uncertainty events which impact on construction time differently, through a combination of the uncertainty events of the individual construction activities. Based on the obtained data, an Adaptive Neuro Fuzzy Inference System (ANFIS) has been developed, as a simple, reliable and accurate advanced machine learning technique to assess the impact of uncertainty events on the completion time of highway construction projects. To validate the ANFIS model, the Stepwise Regression (SR) models have been designed and their results are compared with the results of the ANFIS. Based on the predicted impact size of uncertainty events on the time of highway projects, it can be concluded that construction time on South African highway projects is significantly related to the social and technical uncertainties factors.

关键词： ANFIS     construction time     impact assessment     highway project     South Africa     uncertainty    

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

《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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Comparative study of various artificial intelligence approaches applied to direct torque control of induction motor drives

Moulay Rachid DOUIRI, Mohamed CHERKAOUI

《能源前沿（英文）》 2013年 第7卷 第4期   页码 456-467 doi: 10.1007/s11708-013-0264-8

摘要： In this paper, three intelligent approaches were proposed, applied to direct torque control (DTC) of induction motor drive to replace conventional hysteresis comparators and selection table, namely fuzzy logic, artificial neural network and adaptive neuro-fuzzy inference system (ANFIS). The simulated results obtained demonstrate the feasibility of the adaptive network-based fuzzy inference system based direct torque control (ANFIS-DTC). Compared with the classical direct torque control, fuzzy logic based direct torque control (FL-DTC), and neural networks based direct torque control (NN-DTC), the proposed ANFIS-based scheme optimizes the electromagnetic torque and stator flux ripples, and incurs much shorter execution times and hence the errors caused by control time delays are minimized. The validity of the proposed methods is confirmed by simulation results.

关键词： adaptive neuro-fuzzy inference system (ANFIS)     artificial neural network     direct torque control (DTC)     fuzzy logic     induction motor    

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Application of adaptive neuro-fuzzy inference system and cuckoo optimization algorithm for analyzing electro chemical machining process

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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开采地面沉陷预测的自适应神经模糊推理方法研究

丁德馨,张志军,毕忠伟

《中国工程科学》 2007年 第9卷 第1期   页码 33-39

摘要：

　现行各种开采地面沉陷预测方法均存在着一个共同的缺陷，均不能在集成以往开采地面沉陷工程实 例的基础上对某一地下采矿工程所引起的地面沉陷进行预测，而只能根据某种物理的或力学的方法对其进行预 测。人类在工程实践中所创造的开采地面沉陷方面的经验是非常宝贵的财富，应当在建立开采地面沉陷预测方 法时加以充分利用。以所收集的开采地面沉陷工程实例为基础现行各种开采地面沉陷预测方法均存在着一个共同的缺陷，均不能在集成以往开采地面沉陷工程实 例的基础上对某一地下采矿工程所引起的地面沉陷进行预测，而只能根据某种物理的或力学的

关键词： 地下开采     开采地面沉陷     自适应神经模糊推理系统    

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Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete

Faezehossadat KHADEMI,Mahmoud AKBARI,Sayed Mohammadmehdi JAMAL,Mehdi NIKOO

《结构与土木工程前沿（英文）》 2017年 第11卷 第1期   页码 90-99 doi: 10.1007/s11709-016-0363-9

摘要： Evaluating the in situ concrete compressive strength by means of cores cut from hardened concrete is acknowledged as the most ordinary method, however, it is very difficult to predict the compressive strength of concrete since it is affected by many factors such as different mix designs, methods of mixing, curing conditions, compaction, etc. In this paper, considering the experimental results, three different models of multiple linear regression model (MLR), artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) are established, trained, and tested within the Matlab programming environment for predicting the 28 days compressive strength of concrete with 173 different mix designs. Finally, these three models are compared with each other and resulted in the fact that ANN and ANFIS models enables us to reliably evaluate the compressive strength of concrete with different mix designs, however, multiple linear regression model is not feasible enough in this area because of nonlinear relationship between the concrete mix parameters. Finally, the sensitivity analysis (SA) for two different sets of parameters on the concrete compressive strength prediction are carried out.

关键词： concrete     28 days compressive strength     multiple linear regression     artificial neural network     ANFIS     sensitivity analysis (SA)    

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Using hybrid models to predict blood pressure reactivity to unsupported back based on anthropometric characteristics

Gurmanik KAUR,Ajat Shatru ARORA,Vijender Kumar JAIN

《信息与电子工程前沿（英文）》 2015年 第16卷 第6期   页码 474-485 doi: 10.1631/FITEE.1400295

摘要： Accurate blood pressure (BP) measurement is essential in epidemiological studies, screening programmes, and research studies as well as in clinical practice for the early detection and prevention of high BP-related risks such as coronary heart disease, stroke, and kidney failure. Posture of the participant plays a vital role in accurate measurement of BP. Guidelines on measurement of BP contain recommendations on the position of the back of the participants by advising that they should sit with supported back to avoid spuriously high readings. In this work, principal component analysis (PCA) is fused with forward stepwise regression (SWR), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and the least squares support vector machine (LS-SVM) model for the prediction of BP reactivity to an unsupported back in normotensive and hypertensive participants. PCA is used to remove multi-collinearity among anthropometric predictor variables and to select a subset of components, termed ‘principal components’ (PCs), from the original dataset. The selected PCs are fed into the proposed models for modeling and testing. The evaluation of the performance of the constructed models, using appropriate statistical indices, shows clearly that a PCA-based LS-SVM (PCA-LS-SVM) model is a promising approach for the prediction of BP reactivity in comparison to others. This assessment demonstrates the importance and advantages posed by hybrid models for the prediction of variables in biomedical research studies.

关键词： Blood pressure (BP)     Principal component analysis (PCA)     Forward stepwise regression     Artificial neural network (ANN)     Adaptive neuro-fuzzy inference system (ANFIS)     Least squares support vector machine (LS-SVM)    

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基于GA-ANFIS在石灰矿技术经济系统中的参数优化研究与应用实践

杨仕教,戴剑勇,曾晟

《中国工程科学》 2005年 第7卷 第6期   页码 61-65

摘要：

为掌握水泥原料矿山系统中的技术经济参数对矿石成本影响的关联规律性，首先运用自适应模糊神经网络对矿山技术经济系统建模，再用并行遗传算法对模型求解，得到了确保矿石成本最小的各项最优技术经济指标，为提高矿山生产管理与经济效益提供了重要的参考价值。

关键词： 自适应模糊神经网络     并行遗传算法     技术经济参数    

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标题 作者 时间 类型 操作

基于ANFIS系统的基结构自适应生成

李旲,胡云昌,曹宏铎

期刊论文

基于GA-ANFIS的边坡稳定性评价

林咸志,薛涛,余鹏,陈青

期刊论文

Reactive power compensation of an isolated hybrid power system with load interaction using ANFIS tuned

Nitin SAXENA,Ashwani KUMAR

期刊论文

Enhancement of grid-connected photovoltaic system using ANFIS-GA under different circumstances

Saeed VAFAEI,Alireza REZVANI,Majid GANDOMKAR,Maziar IZADBAKHSH

期刊论文

Novel hybrid models of ANFIS and metaheuristic optimizations (SCE and ABC) for prediction of compressive

Dung Quang VU; Fazal E. JALAL; Mudassir IQBAL; Dam Duc NGUYEN; Duong Kien TRONG; Indra PRAKASH; Binh Thai PHAM

期刊论文

收回说明：使用基于多目标PSO的分层ANFIS控制器-TCSC增强互连多电源系统的动态稳定性

null

期刊论文

Predication of discharge coefficient of cylindrical weir-gate using adaptive neuro fuzzy inference systems (ANFIS

Abbas PARSAIE,Amir Hamzeh HAGHIABI,Mojtaba SANEIE,Hasan TORABI

期刊论文

Key uncertainty events impacting on the completion time of highway construction projects

Alireza MOGHAYEDI, Abimbola WINDAPO

期刊论文

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

期刊论文

Comparative study of various artificial intelligence approaches applied to direct torque control of induction motor drives

Moulay Rachid DOUIRI, Mohamed CHERKAOUI

期刊论文

Application of adaptive neuro-fuzzy inference system and cuckoo optimization algorithm for analyzing electro chemical machining process

Reza TEIMOURI, Hamed SOHRABPOOR

期刊论文

开采地面沉陷预测的自适应神经模糊推理方法研究

丁德馨,张志军,毕忠伟

期刊论文

Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete

Faezehossadat KHADEMI,Mahmoud AKBARI,Sayed Mohammadmehdi JAMAL,Mehdi NIKOO

期刊论文

Using hybrid models to predict blood pressure reactivity to unsupported back based on anthropometric characteristics

Gurmanik KAUR,Ajat Shatru ARORA,Vijender Kumar JAIN

期刊论文

基于GA-ANFIS在石灰矿技术经济系统中的参数优化研究与应用实践

杨仕教,戴剑勇,曾晟

期刊论文