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Statistical process control with intelligence using fuzzy ART neural networks

Min WANG, Tao ZAN, Renyuan FEI,

《机械工程前沿（英文）》 2010年第5卷第2期页码 149-156 doi: 10.1007/s11465-010-0008-y

摘要： With the automation development of manufacturing processes, artificial intelligence technology has been gradually employed to increase the automation and intelligence degree in quality control using statistical process control (SPC) method. In this paper, an SPC method based on a fuzzy adaptive resonance theory (ART) neural network is presented. The fuzzy ART neural network is applied to recognize the special disturbance of the manufacturing processes based on the classification on the histograms, which shows that the fuzzy ART neural network can adaptively learn the features of the histograms of the quality parameters in manufacturing processes. As a result, the special disturbance can be automatically detected when a feature of the special disturbance starts to appear in the histograms. At the same time, combined with spectrum analysis of the autoregressive model of quality parameters, the fuzzy ART neural network can also be utilized to adaptively detect the abnormal patterns in the control chart.

关键词： statistical process control (SPC) fuzzy adaptive resonance theory (ART) histogram control chart time series analysis

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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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An overview on the applications of the hesitant fuzzy sets in group decision-making: Theory, support

Zeshui XU, Shen ZHANG

《工程管理前沿（英文）》 2019年第6卷第2期页码 163-182 doi: 10.1007/s42524-019-0017-4

摘要： Due to the characteristics of hesitant fuzzy sets (HFSs), one hesitant fuzzy element (HFE), which is the basic component of HFSs, can express the evaluation values of multiple decision makers (DMs) on the same alternative under a certain attribute. Thus, the HFS has its unique advantages in group decision making (GDM). Based on which, many scholars have conducted in-depth research on the applications of HFSs in GDM. We have viewed lots of relevant literature and divided the existing studies into three categories: theory, support and methods. In this paper, we elaborate on hesitant fuzzy GDM from these three aspects. The first aspect is mainly about the introduction of HFSs, HFPRs and some hesitant fuzzy aggregation operators. The second aspect describes the consensus process under hesitant fuzzy environment, which is an important support for a complete decision-making process. In the third aspect, we introduce seven hesitant fuzzy GDM approaches, which can be applied in GDM under different decision-making conditions. Finally, we summarize the research status of hesitant fuzzy GDM and put forward some directions of future research.

关键词： hesitant fuzzy set hesitant fuzzy preference relation group decision-making

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Usability perceptions and beliefs about smart thermostats by chi-square test, signal detection theory, and fuzzy detection theory in regions of Mexico

Pedro PONCE, Therese PEFFER, Arturo MOLINA

《能源前沿（英文）》 2019年第13卷第3期页码 522-538 doi: 10.1007/s11708-018-0562-2

摘要： It is well known that smart thermostats (STs) have become key devices in the implementation of smart homes; thus, they are considered as primary elements for the control of electrical energy consumption in households. Moreover, energy consumption is drastically affected when the end users select unsuitable STs or when they do not use the STs correctly. Furthermore, in future, Mexico will face serious electrical energy challenges that can be considerably resolved if the end users operate the STs in a correct manner. Hence, it is important to carry out an in-depth study and analysis on thermostats, by focusing on social aspects that influence the technological use and performance of the thermostats. This paper proposes the use of a signal detection theory (SDT), fuzzy detection theory (FDT), and chi-square (CS) test in order to understand the perceptions and beliefs of end users about the use of STs in Mexico. This paper extensively shows the perceptions and beliefs about the selected thermostats in Mexico. Besides, it presents an in-depth discussion on the cognitive perceptions and beliefs of end users. Moreover, it shows why the expectations of the end users about STs are not met. It also promotes the technological and social development of STs such that they are relatively more accepted in complex electrical grids such as smart grids.

关键词： thermostats perceptions beliefs signal detection theory (SDT) fuzzy signal detection theory (FSDT) chi-square (CS) test

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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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An Ultracompact Spoof Surface Plasmon Sensing System for Adaptive and Accurate Detection of Gas Using

Xuanru Zhang,Jia Wen Zhu,Tie Jun Cui,

《工程（英文）》 doi: 10.1016/j.eng.2023.05.013

摘要： Resonantly enhanced dielectric sensing has superior sensitivity and accuracy because the signal is measured from relative resonance shifts that are immune to signal fluctuations. For applications in the Internet of Things (IoT), accurate detection of resonance frequency shifts using a compact circuit is in high demand. We proposed an ultracompact integrated sensing system that merges a spoof surface plasmon resonance sensor with signal detection, processing, and wireless communication. A software-defined scheme was developed to track the resonance shift, which minimized the hardware circuit and made the detection adaptive to the target resonance. A microwave spoof surface plasmon resonator was designed to enhance sensitivity and resonance intensity. The integrated sensing system was constructed on a printed circuit board with dimensions of 1.8 cm × 1.2 cm and connected to a smartphone wirelessly through Bluetooth, working in both frequency scanning mode and resonance tracking mode and achieving a signal-to-noise ratio of 69 dB in acetone vapor sensing. This study provides an ultracompact, accurate, adaptive, sensitive, and wireless solution for resonant sensors in the IoT.

关键词： Spoof surface plasmons Internet of Things Integrated sensing Resonance tracking Microwave sensing

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the yield of pomegranate oil from supercritical extraction using artificial neural networks and an adaptive-network-basedfuzzy inference system

J. Sargolzaei, A. Hedayati Moghaddam

《化学科学与工程前沿（英文）》 2013年第7卷第3期页码 357-365 doi: 10.1007/s11705-013-1336-3

摘要： Various simulation tools were used to develop an effective intelligent system to predict the effects of temperature and pressure on an oil extraction yield. Pomegranate oil was extracted using a supercritical CO (SC-CO ) process. Several simulation systems including a back-propagation neural network (BPNN), a radial basis function neural network (RBFNN) and an adaptive-network-based fuzzy inference system (ANFIS) were tested and their results were compared to determine the best predictive model. The performance of these networks was evaluated using the coefficient of determination ( ) and the mean square error (MSE). The best correlation between the predicted and the experimental data was achieved using the BPNN method with an of 0.9948.

关键词： oil recovery artificial intelligence extraction neural networks supercritical extraction

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

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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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：在障碍物高度杂乱的未知环境中自主导航是移动机器人研究的一个基本问题。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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Research on Anthropomorphic Obstacle Avoidance Trajectory Planning for Adaptive Driving Scenarios Basedon Inverse Reinforcement Learning Theory

Jian Wu,Yang Yan,Yulong Liu,Yahui Liu,

《工程（英文）》 doi: 10.1016/j.eng.2023.07.018

摘要： The forward design of trajectory planning strategies requires preset trajectory optimization functions, resulting in poor adaptability of the strategy and an inability to accurately generate obstacle avoidance trajectories that conform to real driver behavior habits. In addition, owing to the strong time-varying dynamic characteristics of obstacle avoidance scenarios, it is necessary to design numerous trajectory optimization functions and adjust the corresponding parameters. Therefore, an anthropomorphic obstacle-avoidance trajectory planning strategy for adaptive driving scenarios is proposed. First, numerous expert-demonstrated trajectories are extracted from the HighD natural driving dataset. Subsequently, a trajectory expectation feature-matching algorithm is proposed that uses maximum entropy inverse reinforcement learning theory to learn the extracted expert-demonstrated trajectories and achieve automatic acquisition of the optimization function of the expert-demonstrated trajectory. Furthermore, a mapping model is constructed by combining the key driving scenario information that affects vehicle obstacle avoidance with the weight of the optimization function, and an anthropomorphic obstacle avoidance trajectory planning strategy for adaptive driving scenarios is proposed. Finally, the proposed strategy is verified based on real driving scenarios. The results show that the strategy can adjust the weight distribution of the trajectory optimization function in real time according to the “emergency degree” of obstacle avoidance and the state of the vehicle. Moreover, this strategy can generate anthropomorphic trajectories that are similar to expert-demonstrated trajectories, effectively improving the adaptability and acceptability of trajectories in driving scenarios.

关键词： Obstacle avoidance trajectory planning Inverse reinforcement theory Anthropomorphic Adaptive driving scenarios

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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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生产调度的模糊建模方法研究综述

张虹,李歧强,郭庆强,张鹏,高远

《中国工程科学》 2005年第7卷第12期页码 92-102

摘要：

系统地总结了生产调度问题的各种传统建模方法, 并就模糊数学理论和传统建模及智能方法的结合, 综述了目前生产调度的模糊建模方法的研究进展和成果,提出了几个具有前途和代表性的关于这类问题的研究方法, 以期为这类问题的研究指出可行的道路和方向。

关键词：生产调度模糊数学模糊建模

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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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一种控制规则自调整的模糊控制器

程金,张承慧,夏东伟

《中国工程科学》 2003年第5卷第9期页码 78-81

摘要：

根据模糊控制理论和实际工程经验，设计了一个控制规则能够自调整的模糊控制器，详细介绍了该模糊控制器的控制原理和运行机制，并作出了仿真。该模糊控制器控制精度高，动态和稳态性能均优于传统的PID和基本模糊控制器，且具有较好的鲁棒性和抗扰动能力。仿真和工程实践证明，该模糊控制器具有简便、稳定的优点，且易于工程实现，具有较高的工程应用价值。

关键词：模糊控制控制规则自调整自适应

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标题作者时间类型操作

Statistical process control with intelligence using fuzzy ART neural networks

Min WANG, Tao ZAN, Renyuan FEI,

期刊论文

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

Reza TEIMOURI, Hamed SOHRABPOOR

期刊论文

An overview on the applications of the hesitant fuzzy sets in group decision-making: Theory, support

Zeshui XU, Shen ZHANG

期刊论文

Usability perceptions and beliefs about smart thermostats by chi-square test, signal detection theory, and fuzzy detection theory in regions of Mexico

Pedro PONCE, Therese PEFFER, Arturo MOLINA

期刊论文

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

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

An Ultracompact Spoof Surface Plasmon Sensing System for Adaptive and Accurate Detection of Gas Using

Xuanru Zhang,Jia Wen Zhu,Tie Jun Cui,

期刊论文

the yield of pomegranate oil from supercritical extraction using artificial neural networks and an adaptive-network-basedfuzzy inference system

J. Sargolzaei, A. Hedayati Moghaddam