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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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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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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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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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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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Simulation of foamed concrete compressive strength prediction using adaptive neuro-fuzzy inference system

Ahmad SHARAFATI, H. NADERPOUR, Sinan Q. SALIH, E. ONYARI, Zaher Mundher YASEEN

《结构与土木工程前沿（英文）》 2021年第15卷第1期页码 61-79 doi: 10.1007/s11709-020-0684-6

摘要： Concrete compressive strength prediction is an essential process for material design and sustainability. This study investigates several novel hybrid adaptive neuro-fuzzy inference system (ANFIS) evolutionary models, i.e., ANFIS–particle swarm optimization (PSO), ANFIS–ant colony, ANFIS–differential evolution (DE), and ANFIS–genetic algorithm to predict the foamed concrete compressive strength. Several concrete properties, including cement content (C), oven dry density (O), water-to-binder ratio (W), and foamed volume (F) are used as input variables. A relevant data set is obtained from open-access published experimental investigations and used to build predictive models. The performance of the proposed predictive models is evaluated based on the mean performance (MP), which is the mean value of several statistical error indices. To optimize each predictive model and its input variables, univariate (C, O, W, and F), bivariate (C–O, C–W, C–F, O–W, O–F, and W–F), trivariate (C–O–W, C–W–F, O–W–F), and four-variate (C–O–W–F) combinations of input variables are constructed for each model. The results indicate that the best predictions obtained using the univariate, bivariate, trivariate, and four-variate models are ANFIS–DE– (O) (MP= 0.96), ANFIS–PSO– (C-O) (MP= 0.88), ANFIS–DE– (O–W–F) (MP= 0.94), and ANFIS–PSO– (C–O–W–F) (MP= 0.89), respectively. ANFIS–PSO– (C–O) yielded the best accurate prediction of compressive strength with an MP value of 0.96.

关键词： foamed concrete adaptive neuro fuzzy inference system nature-inspired algorithms prediction of compressive strength

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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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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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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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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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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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使用基于多目标粒子群算法多层自适应模糊推理系统晶闸管控制串联电容器补偿技术的互联多源电力系统动态稳定性增强器 Article

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《信息与电子工程前沿（英文）》 2017年第18卷第3期页码 394-409 doi: 10.1631/FITEE.1500317

摘要：为此，我们使用一种分层自适应神经模糊推理系统控制器－晶闸管可控串联补偿器（hierarchical adaptive neuro-fuzzy inference system controller-TCSC

关键词：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

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基于自适应网络模糊推理系统的移动机器人导航控制器 Research Article

Panati SUBBASH, Kil To CHONG

《信息与电子工程前沿（英文）》 2019年第20卷第2期页码 141-151 doi: 10.1631/FITEE.1700206

摘要：提出一种基于自适应网络模糊推理系统（ANFIS）的差分驱动移动机器人导航控制器，用超声波传感器捕捉移动机器人周围的环境信息。设计了一个基于模糊逻辑的导航控制器，用于获取数据集训练ANFIS控制器。在移动机器人导航过程中，考虑到环境噪声对传感器读数的影响，将加性高斯白噪声添加到传感器读数中并反馈给已训练的ANFIS控制器。在3种不同环境下对移动机器人进行导航，评价该导航控制器的鲁棒性。通过与已有移动机器人导航控制器（如神经网络、模糊逻辑）比较行程长度、行程效率、弯曲能量，验证ANFIS控制器性能。仿真结果表明，与其他控制器相比，ANFIS控制器具有更好性能，能够在不同环境中顺利导航且不与障碍物发生碰撞。

关键词：自适应网络模糊推理系统；加性高斯白噪声；自主导航；移动机器人

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一类非仿射离散非线性系统的直接自适应模糊滑模控制 Article

Xiao-yu ZHANG

《信息与电子工程前沿（英文）》 2016年第17卷第12期页码 1331-1343 doi: 10.1631/FITEE.1500318

摘要：为了获得自适应特性及消除滑模控制抖振，通过使用一个动态模糊逻辑系统（Dynamic fuzzy logical system, DFLS）实现等价控制。DFLS的参数实行在线自调节。

关键词：非线性系统；离散系统；动态模糊逻辑系统；直接自适应；滑模控制

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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 (R_test = 0.923) and minimum root mean square error value (RMSE_test = 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