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A precise tidal prediction mechanism based on the combination of harmonic analysis and adaptive network-based fuzzy inference system model 认领 引用 被引量：6
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作者 ZHANG Zeguo YIN Jianchuan +2 位作者 WANG Nini HU Jiangqiang WANG Ning 《Acta Oceanologica Sinica》 SCIE CAS CSCD 2017年第11期94-105,共12页
An efficient and accurate prediction of a precise tidal level in estuaries and coastal areas is indispensable for the management and decision-making of human activity in the field wok of marine engineering. The variat... An efficient and accurate prediction of a precise tidal level in estuaries and coastal areas is indispensable for the management and decision-making of human activity in the field wok of marine engineering. The variation of the tidal level is a time-varying process. The time-varying factors including interference from the external environment that cause the change of tides are fairly complicated. Furthermore, tidal variations are affected not only by periodic movement of celestial bodies but also by time-varying interference from the external environment. Consequently, for the efficient and precise tidal level prediction, a neuro-fuzzy hybrid technology based on the combination of harmonic analysis and adaptive network-based fuzzy inference system（ANFIS）model is utilized to construct a precise tidal level prediction system, which takes both advantages of the harmonic analysis method and the ANFIS network. The proposed prediction model is composed of two modules： the astronomical tide module caused by celestial bodies’ movement and the non-astronomical tide module caused by various meteorological and other environmental factors. To generate a fuzzy inference system（FIS） structure,three approaches which include grid partition（GP）, fuzzy c-means（FCM） and sub-clustering（SC） are used in the ANFIS network constructing process. Furthermore, to obtain the optimal ANFIS based prediction model, large numbers of simulation experiments are implemented for each FIS generating approach. In this tidal prediction study, the optimal ANFIS model is used to predict the non-astronomical tide module, while the conventional harmonic analysis model is used to predict the astronomical tide module. The final prediction result is performed by combining the estimation outputs of the harmonious analysis model and the optimal ANFIS model. To demonstrate the applicability and capability of the proposed novel prediction model, measured tidal level samples of Fort Pulaski tidal station are selected as the testing database. Simulation and experimental results confirm that the proposed prediction approach can achieve precise predictions for the tidal level with high accuracy, satisfactory convergence and stability. 展开更多
关键词 tidal level prediction harmonious analysis method adaptive network-based fuzzy inference system correlation analysis
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Bottleneck Prediction Method Based on Improved Adaptive Network-based Fuzzy Inference System (ANFIS) in Semiconductor Manufacturing System 认领 引用 被引量：7
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作者 曹政才 邓积杰 +1 位作者 刘民 王永吉 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2012年第6期1081-1088,共8页
Semiconductor manufacturing (SM) system is one of the most complicated hybrid processes involved continuously variable dynamical systems and discrete event dynamical systems. The optimization and scheduling of semicon... Semiconductor manufacturing (SM) system is one of the most complicated hybrid processes involved continuously variable dynamical systems and discrete event dynamical systems. The optimization and scheduling of semiconductor fabrication has long been a hot research direction in automation. Bottleneck is the key factor to a SM system, which seriously influences the throughput rate, cycle time, time-delivery rate, etc. Efficient prediction for the bottleneck of a SM system provides the best support for the consequent scheduling. Because categorical data (product types, releasing strategies) and numerical data (work in process, processing time, utilization rate, buffer length, etc.) have significant effect on bottleneck, an improved adaptive network-based fuzzy inference system (ANFIS) was adopted in this study to predict bottleneck since conventional neural network-based methods accommodate only numerical inputs. In this improved ANFIS, the contribution of categorical inputs to firing strength is reflected through a transformation matrix. In order to tackle high-dimensional inputs, reduce the number of fuzzy rules and obtain high prediction accuracy, a fuzzy c-means method combining binary tree linear division method was applied to identify the initial structure of fuzzy inference system. According to the experimental results, the main-bottleneck and sub-bottleneck of SM system can be predicted accurately with the proposed method. 展开更多
关键词 semiconductor manufacturing system bottleneck prediction adaptive network-based fuzzy inference system
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Adaptive neuro fuzzy inference system for classification of water quality status 认领 引用 被引量：11
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作者 Han Yan Zhihong Zou Huiwen Wang 《Journal of Environmental Sciences》 SCIE EI CAS 2010年第12期1891-1896,共6页
An adaptive neuro fuzzy inference system was used for classifying water quality status of river.It applied several physical and inorganic chemical indicators including dissolved oxygen,chemical oxygen demand,and ammon... An adaptive neuro fuzzy inference system was used for classifying water quality status of river.It applied several physical and inorganic chemical indicators including dissolved oxygen,chemical oxygen demand,and ammonia-nitrogen.A data set(nine weeks,total 845 observations)was collected from 100 monitoring stations in all major river basins in China and used for training and validating the model.Up to 89.59%of the data could be correctly classified using this model.Such performance was more competitive when compared with artificial neural networks.It is applicable in evaluation and classification of water quality status. 展开更多
关键词 adaptive neuro fuzzy inference system artificial neural networks water quality status classification
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A reversibly used cooling tower with adaptive neuro-fuzzy inference system 认领 引用 被引量：3
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作者 吴加胜 张国强 +3 位作者 张泉 周晋 郭永辉 沈炜 《Journal of Central South University》 SCIE EI CAS 2012年第3期715-720,共6页
An adaptive neuro-fuzzy inference system(ANFIS) for predicting the performance of a reversibly used cooling tower(RUCT) under cross flow conditions as part of a heat pump system for a heating mode in winter was demons... An adaptive neuro-fuzzy inference system(ANFIS) for predicting the performance of a reversibly used cooling tower(RUCT) under cross flow conditions as part of a heat pump system for a heating mode in winter was demonstrated.Extensive field experimental work was carried out in order to gather enough data for training and prediction.The statistical methods,such as the correlation coefficient,absolute fraction of variance and root mean square error,were given to compare the predicted and actual values for model validation.The simulation results predicted with the ANFIS can be used to simulate the performance of a reversibly used cooling tower quite accurately.Therefore,the ANFIS approach can reliably be used for forecasting the performance of RUCT. 展开更多
关键词 reversibly used cooling tower heating adaptive neuro-fuzzy inference system fuzzy modeling approach
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APPLICATION STUDY ON ADAPTIVE NEURAL FUZZY INFERENCE MODEL IN COMPLEX SOCIAL-TECHNICAL SYSTEM 认领 引用
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作者 冯绍红 李东 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2011年第4期393-399,共7页
The adaptive neural fuzzy inference system （ANFIS） is used to make a ease study considering features of complex social-technical system with the target of increasing organizational efficiency of public scientific re... The adaptive neural fuzzy inference system （ANFIS） is used to make a ease study considering features of complex social-technical system with the target of increasing organizational efficiency of public scientific research institutions. An integrated ANFIS model is built and the effectiveness of the model is verified by means of investigation data and their processing results. The model merges the learning mechanism of neural network and the language inference ability of fuzzy system, and thereby remedies the defects of neural network and fuzzy logic system. Result of this case study shows that the model is suitable for complicated socio-technical systems and has bright application perspective to solve such problems of prediction, evaluation and policy-making in managerial fields. 展开更多
关键词 complex adaptive system adaptive neural fuzzy inference system （ANFIS） complex social-technical system organizational efficiency
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Experimental investigation and adaptive neural fuzzy inference system prediction of copper recovery from flotation tailings by acid leaching in a batch agitated tank 认领 引用 被引量：3
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作者 Jalil Pazhoohan Hossein Beiki Morteza EsfANDyari 《International Journal of Minerals,Metallurgy and Materials》 SCIE EI CAS CSCD 2019年第5期538-546,共9页
The potential of copper recovery from flotation tailings was experimentally investigated using a laboratory-mixing tank. The experiments were performed with solid weight percentages of 30 wt%, 35 wt%, 40 wt% and 45 wt... The potential of copper recovery from flotation tailings was experimentally investigated using a laboratory-mixing tank. The experiments were performed with solid weight percentages of 30 wt%, 35 wt%, 40 wt% and 45 wt% in water. The measurements revealed that adding sulfuric acid all at once to the tank rapidly increased the efficiency of the leaching process, which was attributed to the rapid change in the acid concentration. The rate of iron dissolution from tailings was less than when the acid was added gradually. The sample with 40 wt% solid is recommended as an appropriate feed for the recovery of copper. The adaptive neural fuzzy system(ANFIS) was also used to predict the copper recovery from flotation tailings. The back-propagation algorithm and least squares method were applied for the training of ANFIS. The validation data was also applied to evaluate the performance of these models. Simulation results revealed that the testing results from these models were in good agreement with the experimental data. 展开更多
关键词 flotation tailings leaching copper environments adaptive neural fuzzy inference system
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Estimation of convergence of a high-speed railway tunnel in weak rocks using an adaptive neuro-fuzzy inference system(ANFIS) approach 认领 引用 被引量：2
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作者 A.C.Adoko Li Wu 《Journal of Rock Mechanics and Geotechnical Engineering》 2012年第1期11-18,共8页
Estimation of tunnel diameter convergence is a very important issue for tunneling construction,especially when the new Austrian tunneling method（NATM） is adopted.For this purpose,a systematic convergence measurement... Estimation of tunnel diameter convergence is a very important issue for tunneling construction,especially when the new Austrian tunneling method（NATM） is adopted.For this purpose,a systematic convergence measurement is usually implemented to adjust the design during the whole construction,and consequently deadly hazards can be prevented.In this study,a new fuzzy model capable of predicting the diameter convergences of a high-speed railway tunnel was developed on the basis of adaptive neuro-fuzzy inference system（ANFIS） approach.The proposed model used more than 1 000 datasets collected from two different tunnels,i.e.Daguan tunnel No.2 and Yaojia tunnel No.1,which are part of a tunnel located in Hunan Province,China.Six Takagi-Sugeno fuzzy inference systems were constructed by using subtractive clustering method.The data obtained from Daguan tunnel No.2 were used for model training,while the data from Yaojia tunnel No.1 were employed to evaluate the performance of the model.The input parameters include surrounding rock masses（SRM） rating index,ground engineering conditions（GEC） rating index,tunnel overburden（H）,rock density（？）,distance between monitoring station and working face（D）,and elapsed time（T）.The model’s performance was assessed by the variance account for（VAF）,root mean square error（RMSE）,mean absolute percentage error（MAPE） as well as the coefficient of determination（R2） between measured and predicted data as recommended by many researchers.The results showed excellent prediction accuracy and it was suggested that the proposed model can be used to estimate the tunnel convergence and convergence velocity. 展开更多
关键词 tunnel convergence prediction new Austrian tunneling method （NATM） adaptive neurc,-fuzzy inference system（ANF1S） subtractive clustering
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An Adaptive Neuro-Fuzzy Inference System to Improve Fractional Order Controller Performance 认领 引用
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作者 N.Kanagaraj 《Intelligent Automation & Soft Computing》 SCIE 2023年第3期3213-3226,共14页
The design and analysis of a fractional order proportional integral deri-vate(FOPID)controller integrated with an adaptive neuro-fuzzy inference system(ANFIS)is proposed in this study.Afirst order plus delay time plant... The design and analysis of a fractional order proportional integral deri-vate(FOPID)controller integrated with an adaptive neuro-fuzzy inference system(ANFIS)is proposed in this study.Afirst order plus delay time plant model has been used to validate the ANFIS combined FOPID control scheme.In the pro-posed adaptive control structure,the intelligent ANFIS was designed such that it will dynamically adjust the fractional order factors(λandµ)of the FOPID(also known as PIλDµ)controller to achieve better control performance.When the plant experiences uncertainties like external load disturbances or sudden changes in the input parameters,the stability and robustness of the system can be achieved effec-tively with the proposed control scheme.Also,a modified structure of the FOPID controller has been used in the present system to enhance the dynamic perfor-mance of the controller.An extensive MATLAB software simulation study was made to verify the usefulness of the proposed control scheme.The study has been carried out under different operating conditions such as external disturbances and sudden changes in input parameters.The results obtained using the ANFIS-FOPID control scheme are also compared to the classical fractional order PIλDµand conventional PID control schemes to validate the advantages of the control-lers.The simulation results confirm the effectiveness of the ANFIS combined FOPID controller for the chosen plant model.Also,the proposed control scheme outperformed traditional control methods in various performance metrics such as rise time,settling time and error criteria. 展开更多
关键词 Adaptive neuro-fuzzy inference system(ANFIS) fuzzy logic controller fractional order control PID controller first order time delay system
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Sleep Apnea Detection Using Adaptive Neuro Fuzzy Inference System 认领 引用
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作者 Cafer Avci Gokhan Bilgin 《Engineering（科研）》 2013年第10期259-263,共5页
This paper presents an efficient and easy implemented method for detecting minute based analysis of sleep apnea. The nasal, chest and abdominal based respiratory signals extracted from polysomnography recordings are o... This paper presents an efficient and easy implemented method for detecting minute based analysis of sleep apnea. The nasal, chest and abdominal based respiratory signals extracted from polysomnography recordings are obtained from PhysioNet apnea-ECG database. Wavelet transforms are applied on the 1-minute and 3-minute length recordings. According to the preliminary tests, the variances of 10th and 11th detail components can be used as discriminative features for apneas. The features obtained from total 8 recordings are used for training and testing of an adaptive neuro fuzzy inference system (ANFIS). Training and testing process have been repeated by using the randomly obtained five different sequences of whole data for generalization of the ANFIS. According to results, ANFIS based classification has sufficient accuracy for apnea detection considering of each type of respiratory. However, the best result is obtained by analyzing the 3-minute length nasal based respiratory signal. In this study, classification accuracies have been obtained greater than 95.2% for each of the five sequences of entire data. 展开更多
关键词 Sleep Apnea Wavelet Decomposition Adaptive Neuro Fuzzy Inference System
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Temperature modeling and control of Direct Methanol Fuel Cell based on adaptive neural fuzzy technology 认领 引用
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作者 戚志东 Zhu Xinjian Cao Guangyi 《High Technology Letters》 EI CAS 2006年第4期421-426,共6页
Aiming at on-line controlling of Direct Methanol Fuel Cell （DMFC） stack, an adaptive neural fuzzy inference technology is adopted in the modeling and control of DMFC temperature system. In the modeling process, an A... Aiming at on-line controlling of Direct Methanol Fuel Cell （DMFC） stack, an adaptive neural fuzzy inference technology is adopted in the modeling and control of DMFC temperature system. In the modeling process, an Adaptive Neural Fuzzy Inference System （ANFIS） identification model of DMFC stack temperature is developed based on the input-output sampled data, which can avoid the internal complexity of DMFC stack. In the controlling process, with the network model trained well as the reference model of the DMFC control system, a novel fuzzy genetic algorithm is used to regulate the parameters and fuzzy rules of a neural fuzzy controller. In the simulation, compared with the nonlinear Proportional Integral Derivative （PID） and traditional fuzzy algorithm, the improved neural fuzzy controller designed in this paper gets better performance, as demonstrated by the simulation results. 展开更多
关键词 direct methanol fuel cell （DMFC） adaptive neural fuzzy inference technology fuzzy genetic algorithms （FGA）
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Identification and novel adaptive fuzzy control of nonlinear system for PEMFC stack 认领 引用
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作者 卫东 许宏 朱新坚 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2006年第2期186-192,共7页
The operating temperature of a proton exchange membrane fuel cell stack is a very important control parameter. It should be controlled within a specific range, however, most of existing PEMFC mathematical models are t... The operating temperature of a proton exchange membrane fuel cell stack is a very important control parameter. It should be controlled within a specific range, however, most of existing PEMFC mathematical models are too complicated to be effectively applied to on-line control. In this paper, input-output data and operating experiences will be used to establish PEMFC stack model and operating temperature control system. An adaptive learning algorithm and a nearest-neighbor clustering algorithm are applied to regulate the parameters and fuzzy rules so that the model and the control system are able to obtain higher accuracy. In the end, the simulation and the experimental results are presented and compared with traditional PID and fuzzy control algorithms. 展开更多
关键词 proton exchange membrane fuel cell （PEMFC） adaptive neural-networks fuzzy infer system （ ANFIS） adaptive neural-network learning algorithm （ANA） nearest-neighbor clustering algorithm （NCA）
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Simulation of foamed concrete compressive strength prediction using adaptive neuro-fuzzy inference system optimized by nature-inspired algorithms 认领 引用 被引量：5
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作者 Ahmad SHARAFATI H.NADERPOUR +2 位作者 Sinan Q.SALIH E.ONYARI Zaher Mundher YASEEN 《Frontiers of Structural and Civil Engineering》 SCIE EI CSCD 2021年第1期61-79,共19页
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.... 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. 展开更多
关键词 foamed concrete adaptive neuro fuzzy inference system nature-inspired algorithms prediction of compressive strength
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Adaptive network fuzzy inference system based navigation controller for mobile robotAdaptive network fuzzy inference system based navigation controller for mobile robot 认领 引用 被引量：1
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作者 Panati SUBBASH Kil To CHONG 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2019年第2期141-151,共11页
Autonomous navigation of a mobile robot in an unknown environment with highly cluttered obstacles is a fundamental issue in mobile robotics research. We propose an adaptive network fuzzy inference system(ANFIS) based ... Autonomous navigation of a mobile robot in an unknown environment with highly cluttered obstacles is a fundamental issue in mobile robotics research. We propose an adaptive network fuzzy inference system(ANFIS) based navigation controller for a differential drive mobile robot in an unknown environment with cluttered obstacles. Ultrasonic sensors are used to capture the environmental information around the mobile robot. A training data set required to train the ANFIS controller has been obtained by designing a fuzzy logic based navigation controller. Additive white Gaussian noise has been added to the sensor readings and fed to the trained ANFIS controller during mobile robot navigation, to account for the effect of environmental noise on sensor readings. The robustness of the proposed navigation controller has been evaluated by navigating the mobile robot in three different environments. The performance of the proposed controller has been verified by comparing the travelled path length/efficiency and bending energy obtained by the proposed method with reference mobile robot navigation controllers, such as neural network, fuzzy logic, and ANFIS. Simulation results presented in this paper show that the proposed controller has better performance compared with reference controllers and can successfully navigate in different environments without any collision with obstacles. 展开更多
关键词 Adaptive network fuzzy inference system Additive white Gaussian noise Autonomous navigation Mobile robot
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激光测距与模糊推理的机械臂驱动关节防碰撞控制 认领 引用
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作者 宋珍珍 李攀攀 辛艳粉 《激光杂志》 CAS 北大核心 2026年第1期245-251,共7页
为了降低机械手臂碰撞风险,设计基于激光测距与模糊推理的机械臂驱动关节防碰撞控制方法。脉冲式激光测距装置实现了机械手臂运行中驱动关节与其他机械手臂、障碍物等的精确相对距离,将激光测距装置测量的相对距离与安全距离之差以及相... 为了降低机械手臂碰撞风险,设计基于激光测距与模糊推理的机械臂驱动关节防碰撞控制方法。脉冲式激光测距装置实现了机械手臂运行中驱动关节与其他机械手臂、障碍物等的精确相对距离,将激光测距装置测量的相对距离与安全距离之差以及相对速度作为输入,利用自适应神经模糊推理控制模型控制机械手臂驱动关节的运动速度,确保机械手臂在作业过程中能够避免与其他物品发生碰撞,实现其驱动关节的防碰撞模糊控制。测试结果表明,在动态环境下,障碍物距离为0.5 m时,所提方法的延迟时间为10.2 ms,2.5 m时仅为13.8 ms,能够实时、稳定地避免碰撞,满足复杂动态环境下的机械手臂防碰撞控制需求。 展开更多
关键词 激光测距 机械手臂 驱动关节 防碰撞 自适应神经模糊推理控制模型
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基于自适应神经模糊推理与反馈数据驱动储能变流器电容老化指征参数监测方法 认领 引用
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作者 田艳军 翟海智 +1 位作者 屈章顺 刘青 《电工技术学报》 EI CSCD 北大核心 2026年第14期4960-4973,共14页
电容元件在变流器中存在电解液干涸、介质老化等短板,其寿命与可靠性受温度、纹波电流等多因素耦合影响,成为制约变流器整体寿命与安全可靠运行的关键因素。为有效监测储能变流器电容元件老化状态,该文提出一种基于自适应神经模糊推理... 电容元件在变流器中存在电解液干涸、介质老化等短板,其寿命与可靠性受温度、纹波电流等多因素耦合影响,成为制约变流器整体寿命与安全可靠运行的关键因素。为有效监测储能变流器电容元件老化状态,该文提出一种基于自适应神经模糊推理系统的电容关键状态参数在线辨识方法,该方法旨在实时监测电容的当前状态,为老化评估提供精确的数据基础。首先,分析电容物理结构及储能变流器应用场景,并建立电容老化指征模型。其次,基于自适应神经模糊推理算法的梯度优点建立电容-电阻参数辨识自适应模型,并用辨识参数更新电容特性方程,以微分电压误差实现参数的在线评估,与传统递推最小二乘法结果相比,该方法采用自适应神经模糊推理系统神经网络提高电容参数辨识的准确度,并通过在线数据反馈进行有效评估。最后,搭建仿真模型与实验平台,开展仿真与实验,并且通过实验辨识与评估结果对比在正常工况与噪声场景下验证了所提方法的可行性与有效性。 展开更多
关键词 滤波电容 储能变流器 参数辨识 自适应神经模糊推理系统
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基于ANFIS的大坝地震动PGA预测模型研究 认领 引用
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作者 陈辉 柳存喜 杜兴忠 《人民长江》 北大核心 2026年第8期201-211,共11页
为了能够在震后快速为大坝安全评估及时提供地震峰值加速度(PGA)数据支持,提高地震应急响应效率,基于自适应神经模糊推理系统(ANFIS)构建了一种综合考虑震源参数(包括震级、方位角、深度)、传播路径参数(震中距)以及场地响应特征(测点P... 为了能够在震后快速为大坝安全评估及时提供地震峰值加速度(PGA)数据支持,提高地震应急响应效率,基于自适应神经模糊推理系统(ANFIS)构建了一种综合考虑震源参数(包括震级、方位角、深度)、传播路径参数(震中距)以及场地响应特征(测点PGA)的大坝地震动PGA预测模型,形成一个多维度的非线性预测框架。以大岗山水电站周边350 km范围内记录的500次地震事件实测数据为基础,对ANFIS、随机森林、极端随机树、岭回归等23种方法的模型预测性能进行了比较分析。研究结果表明:ANFIS模型在测试数据上的决定系数(R2)为0.827,相较于其它预测模型预测效果突出,具有较高的训练效率,并且展现出更出色的泛化能力;ANFIS模型在预测大坝地震动PGA时,具有更高的预测精度和出色的稳定性,可以作为大坝结构地震安全快速评估的一种有效方法。 展开更多
关键词 自适应神经模糊推理系统(ANFIS) 地震峰值加速度 大坝地震动 机器学习
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基于改进ANFIS的电力电容器绝缘状态检测方法 认领 引用
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作者 吴天博 刘磊 +3 位作者 葛志杰 韩雪峰 张维宁 王健 《电测与仪表》 CSCD 北大核心 2026年第5期193-200,共8页
针对现有电力电容器绝缘状态检测方法存在的检测精度差和效率低等问题,在对电力电容器在线监测系统进行分析的基础上,提出一种用于电力电容器绝缘状态检测的改进自适应神经模糊推理系统(adaptive network-based fuzzy inference system,... 针对现有电力电容器绝缘状态检测方法存在的检测精度差和效率低等问题,在对电力电容器在线监测系统进行分析的基础上,提出一种用于电力电容器绝缘状态检测的改进自适应神经模糊推理系统(adaptive network-based fuzzy inference system,ANFIS),通过改进粒子群算法对自适应神经模糊推理系统参数进行寻优,提高了模型检测准确率和降低了模型训练时间。通过算例对所提方法性能进行分析,验证其有效性和优越性。结果表明,与常规检测方法相比,所提检测方法的检测结果基本与实际绝缘状态水平相同,能够准确地检测出绝缘状态,检测速度能够满足实时检测的需要,检测精度更高。 展开更多
关键词 电力电容器 绝缘状态检测 粒子群算法 自适应神经模糊推理系统 在线监测系统
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重载自动地面引导车的自适应模糊神经网络系统补偿式主动油气悬架分层控制研究 认领 引用
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作者 李霞 高亚楠 +2 位作者 高琳 何涛 刘本学 《液压与气动》 北大核心 2026年第2期64-73,共10页
为提升重载自动地面引导车在非结构路面下的姿态控制性能,针对油气悬架强非线性及电磁阀控制信号-主动力非线性导致的力输出精度低的问题,提出模糊神经网络系统补偿的分层控制策略。上层以线性二次型调节实现车身垂向全局最优控制,结合... 为提升重载自动地面引导车在非结构路面下的姿态控制性能,针对油气悬架强非线性及电磁阀控制信号-主动力非线性导致的力输出精度低的问题,提出模糊神经网络系统补偿的分层控制策略。上层以线性二次型调节实现车身垂向全局最优控制,结合模糊PID抑制侧倾,共同生成期望主动力控制力;下层利用模糊神经网络系统对力跟踪误差进行学习与补偿,建立从力误差到电磁阀控制信号的非线性映射关系,从而实现高精度力跟踪控制。仿真表明,该策略可有效改善矿用自动地面引导车在非结构路面下的姿态响应,在C级随机路面激励下,车身垂向、侧倾角加速度均方根值较被动悬架分别降低30.4%、38.9%;单侧阶跃路面激励下,车身侧倾峰值较模糊PID补偿减小44%,振动更平稳,验证了其复杂工况下的优越性与鲁棒性。 展开更多
关键词 重载自动地面引导车 非结构路面 油气悬架 自适应模糊神经网络系统 非线性补偿 AMESim&MATLAB联合仿真
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基于姿态检测与Sugeno模糊推理的速率自适应算法 认领 引用
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作者 夏巍 荣凯 马驻 《光通信技术》 北大核心 2026年第1期42-46,共5页
为解决水下湍流导致光束偏移、通信链路不稳定的问题,提出一种基于姿态检测与Sugeno模糊推理的速率自适应方法。通过MPU9250姿态传感器实时采集设备三维姿态数据,经差值运算得到相对角度偏差,并将其作为模糊控制器的输入变量,依据预设... 为解决水下湍流导致光束偏移、通信链路不稳定的问题,提出一种基于姿态检测与Sugeno模糊推理的速率自适应方法。通过MPU9250姿态传感器实时采集设备三维姿态数据,经差值运算得到相对角度偏差,并将其作为模糊控制器的输入变量,依据预设模糊规则动态匹配最优通信速率等级,实现收发两端速率同步调整。实验结果表明,该方法可在Y、Z轴偏移角度变化时自动切换通信速率(从460800 b/s降至1200 b/s),有效维持链路连通性,显著提升水下可见光通信的可靠性与鲁棒性。 展开更多
关键词 水下可见光通信 通信速率自适应 姿态检测 三维姿态 Sugeno模糊推理模型
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Discrimination of quarry blasts and microearthquakes using adaptive neuro-fuzzy inference systems in the Tehran region 认领 引用
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作者 Jamileh Vasheghani Farahani 《Episodes》 SCIE 2015年第3期162-168,共7页
The purpose of this research is to demonstrate the use of Adaptive Neuro-Fuzzy Inference System(ANFIS)for discrimination between quarry blasts and microearthquakes in the Tehran region using data from the Broadband Ir... The purpose of this research is to demonstrate the use of Adaptive Neuro-Fuzzy Inference System(ANFIS)for discrimination between quarry blasts and microearthquakes in the Tehran region using data from the Broadband Iranian National Network Center(BIN).In the south and southeast of Tehran,a large number of quarry blasts“contaminate”the earthquake catalog.In order to identify the real seismicity(tectonic earthquakes)in the region,we need to discriminate quarry blasts from natural earthquakes in the catalog. 展开更多
关键词 quarry blasts quarry blasts contaminate adaptive neuro fuzzy inference system microearthquakes ANFIS identify real seismicity tectonic earthquakes discriminate quarry blasts broadband iranian national network
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