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APPLICATION STUDY ON ADAPTIVE NEURAL FUZZY INFERENCE MODEL IN COMPLEX SOCIAL-TECHNICAL SYSTEM
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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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Neural Network Based Adaptive Tracking of Nonlinear Multi-Agent System 被引量:1
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作者 Bo-Xian Lin Wei-Hao Li +1 位作者 Kai-Yu Qin Xi Chen 《Journal of Electronic Science and Technology》 CAS CSCD 2021年第2期144-154,共11页
In this paper,the problems of robust consensus tracking control for the second-order multi-agent system with uncertain model parameters and nonlinear disturbances are considered.An adaptive control strategy is propose... In this paper,the problems of robust consensus tracking control for the second-order multi-agent system with uncertain model parameters and nonlinear disturbances are considered.An adaptive control strategy is proposed to smooth the agent’s trajectory,and the neural network is constructed to estimate the system’s unknown components.The consensus conditions are demonstrated for tracking a leader with nonlinear dynamics under an adaptive control algorithm in the absence of model uncertainties.Then,the results are extended to the system with unknown time-varying disturbances by applying the neural network estimation to compensating for the uncertain parts of the agents’models.Update laws are designed based on the Lyapunov function terms to ensure the effectiveness of robust control.Finally,the theoretical results are verified by numerical simulations,and a comparative experiment is conducted,showing that the trajectories generated by the proposed method exhibit less oscillation and converge faster. 展开更多
关键词 Coordinated tracking leader following consensus neural network based adaptive control robust control uncertain nonlinear system
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Adaptive fuzzy synchronization for a class of fractional-order neural networks 被引量:1
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作者 刘恒 李生刚 +1 位作者 王宏兴 李冠军 《Chinese Physics B》 SCIE EI CAS CSCD 2017年第3期258-267,共10页
In this paper, synchronization for a class of uncertain fractional-order neural networks with external disturbances is discussed by means of adaptive fuzzy control. Fuzzy logic systems, whose inputs are chosen as sync... In this paper, synchronization for a class of uncertain fractional-order neural networks with external disturbances is discussed by means of adaptive fuzzy control. Fuzzy logic systems, whose inputs are chosen as synchronization errors, are employed to approximate the unknown nonlinear functions. Based on the fractional Lyapunov stability criterion, an adaptive fuzzy synchronization controller is designed, and the stability of the closed-loop system, the convergence of the synchronization error, as well as the boundedness of all signals involved can be guaranteed. To update the fuzzy parameters, fractional-order adaptations laws are proposed. Just like the stability analysis in integer-order systems, a quadratic Lyapunov function is used in this paper. Finally, simulation examples are given to show the effectiveness of the proposed method. 展开更多
关键词 fractional-order neural network adaptive fuzzy control fractional-order adaptation law
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Design of robust fuzzy controller for ship course-tracking based on RBF network and backstepping approach 被引量:4
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作者 ZHANG Song-tao REN Guang 《Journal of Marine Science and Application》 2006年第3期5-10,共6页
This study presents an adaptive fuzzy neural network (FNN) control system for the ship steering autopilot. For the Norrbin ship steering mathematical model with the nonlinear and uncertain dynamic characteristics, an ... This study presents an adaptive fuzzy neural network (FNN) control system for the ship steering autopilot. For the Norrbin ship steering mathematical model with the nonlinear and uncertain dynamic characteristics, an adaptive FNN control system is designed to achieve high-precision track control via the backstepping approach. In the adaptive FNN control system, a FNN backstepping controller is a principal controller which includes a FNN estimator used to estimate the uncertainties, and a robust controller is designed to compensate the shortcoming of the FNN backstepping controller. All adaptive learning algorithms in the adaptive FNN control system are derived from the sense of Lyapunov stability analysis, so that system-tracking stability can be guaranteed in the closed-loop system. The effectiveness of the proposed adaptive FNN control system is verified by simulation results. 展开更多
关键词 fuzzy neural network ship course-tracking adaptive control backstepping approach
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Detection of Coal Mine Spontaneous Combustion by Fuzzy Inference System 被引量:1
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作者 SUN Ji-ping SONG Shu +1 位作者 MA Feng-ying ZHANG Ya-li 《Journal of China University of Mining and Technology》 EI 2006年第3期258-260,265,共4页
The spontaneous combustion is a smoldering process and characterized by a slow burning speed and a long duration. Therefore, it is a hazard to coal mines. Early detection of coal mine spontaneous combustion is quite d... The spontaneous combustion is a smoldering process and characterized by a slow burning speed and a long duration. Therefore, it is a hazard to coal mines. Early detection of coal mine spontaneous combustion is quite difficult because of the complexity of different coal mines. And the traditional threshold discriminance is not suitable for spontaneous combustion detection due to the uncertainty of coalmine combustion. Restrictions of the single detection method will also affect the detection precision in the early time of spontaneous combustion. Although multiple detection methods can be adopted as a complementarity to improve the accuracy of detection, the synthesized method will in- crease the complicacy of criterion, making it difficult to estimate the combustion. To solve this problem, a fuzzy inference system based on CRI (Compositional Rule of Inference) and fuzzy reasoning method FITA (First Infer Then Aggregate) are presented. And the neural network is also developed to realize the fuzzy inference system. Finally, the effectiveness of the inference system is demonstrated bv means of an experiment. 展开更多
关键词 spontaneous combustion fuzzy inference system CRI FITA neural network
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Using genetic algorithm based fuzzy adaptive resonance theory for clustering analysis 被引量:3
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作者 LIU Bo WANG Yong WANG Hong-jian 《哈尔滨工程大学学报》 EI CAS CSCD 北大核心 2006年第B07期547-551,共5页
关键词 聚类分析 遗传算法 模糊自适应谐振理论 人工神经网络
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Additive-Multiplicative Fuzzy Neural Network and Its Performance
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作者 翟东海 靳蕃 《Journal of Southwest Jiaotong University(English Edition)》 2003年第1期16-22,共7页
In view of the main weaknesses of current fuzzy neural networks such as low reasoning precision and long training time, an Additive Multiplicative Fuzzy Neural Network (AMFNN) model and its architecture are present... In view of the main weaknesses of current fuzzy neural networks such as low reasoning precision and long training time, an Additive Multiplicative Fuzzy Neural Network (AMFNN) model and its architecture are presented. AMFNN combines additive inference and multiplicative inference into an integral whole, reasonably makes use of their advantages of inference and effectively overcomes their weaknesses when they are used for inference separately. Here, an error back propagation algorithm for AMFNN is presented based on the gradient descent method. Comparisons between the AMFNN and six representative fuzzy inference methods shows that the AMFNN is characterized by higher reasoning precision, wider application scope, stronger generalization capability and easier implementation. 展开更多
关键词 fuzzy inference additive multiplicative fuzzy neural network fuzzy rule acquisition
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Employing adaptive fuzzy computing for RCP intelligent control and fault diagnosis 被引量:1
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作者 Ashraf Aboshosha Hisham A.Hamad 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2023年第9期82-93,共12页
Loss of coolant accident(LOCA),loss of fluid accident(LOFA),and loss of vacuum accident(LOVA)are the most severe accidents that can occur in nuclear power reactors(NPRs).These accidents occur when the reactor loses it... Loss of coolant accident(LOCA),loss of fluid accident(LOFA),and loss of vacuum accident(LOVA)are the most severe accidents that can occur in nuclear power reactors(NPRs).These accidents occur when the reactor loses its cooling media,leading to uncontrolled chain reactions akin to a nuclear bomb.This article is focused on exploring methods to prevent such accidents and ensure that the reactor cooling system remains fully controlled.The reactor coolant pump(RCP)has a pivotal role in facilitating heat exchange between the primary cycle,which is connected to the reactor core,and the secondary cycle associated with the steam generator.Furthermore,the RCP is integral to preventing catastrophic events such as LOCA,LOFA,and LOVA accidents.In this study,we discuss the most critical aspects related to the RCP,specifically focusing on RCP control and RCP fault diagnosis.The AI-based adaptive fuzzy method is used to regulate the RCP’s speed and torque,whereas the neural fault diagnosis system(NFDS)is implemented for alarm signaling and fault diagnosis in nuclear reactors.To address the limitations of linguistic and statistical intelligence approaches,an integration of the statistical approach with fuzzy logic has been proposed.This integrated system leverages the strengths of both methods.Adaptive fuzzy control was applied to the VVER 1200 NPR-RCP induction motor,and the NFDS was implemented on the Kori-2 NPR-RCP. 展开更多
关键词 Nuclear power plant(NPP) Reactor coolant pump Fault diagnosis Reactor passive safety neural network adaptive fuzzy
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A New Neuro-Fuzzy Adaptive Genetic Algorithm
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作者 ZHU Lili ZHANG Huanchun JING Yazhi(Faculty 302,Nanjing University of Aeronautics and Astronautics,Nanjing 210016 China) 《Journal of Electronic Science and Technology of China》 2003年第1期63-68,共6页
Novel neuro-fuzzy techniques are used to dynamically control parameter settings ofgenetic algorithms (GAs).The benchmark routine is an adaptive genetic algorithm (AGA) that uses afuzzy knowledge-based system to contro... Novel neuro-fuzzy techniques are used to dynamically control parameter settings ofgenetic algorithms (GAs).The benchmark routine is an adaptive genetic algorithm (AGA) that uses afuzzy knowledge-based system to control GA parameters.The self-learning ability of the cerebellar modelariculation controller (CMAC) neural network makes it possible for on-line learning the knowledge onGAs throughout the run.Automatically designing and tuning the fuzzy knowledge-base system,neuro-fuzzy techniques based on CMAC can find the optimized fuzzy system for AGA by the renhanced learningmethod.The Results from initial experiments show a Dynamic Parametric AGA system designed by theproposed automatic method and indicate the general applicability of the neuro-fuzzy AGA to a widerange of combinatorial optimization. 展开更多
关键词 genetic algorithm fuzzy logic control CMAC neural network adaptive parameter control
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生活垃圾焚烧智能控制方法研究
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作者 丁海霞 李爱民 刘传群 《大连理工大学学报》 北大核心 2025年第3期235-242,共8页
焚烧因其减容、减量及能源回收利用等优势而成为生活垃圾主要处理技术.为了解决生活垃圾焚烧过程控制中多个运行操作参数调节困难的问题,利用机器学习(ML)对垃圾焚烧过程中的运行操作参数和控制变量进行高精度非线性映射,达到根据控制... 焚烧因其减容、减量及能源回收利用等优势而成为生活垃圾主要处理技术.为了解决生活垃圾焚烧过程控制中多个运行操作参数调节困难的问题,利用机器学习(ML)对垃圾焚烧过程中的运行操作参数和控制变量进行高精度非线性映射,达到根据控制变量要求来自动定量调节运行操作参数的目的.采用神经网络和模糊推理系统算法来构建拟合控制模型,基于Aspen Plus垃圾焚烧模拟数据的训练和验证,得出的最优模型为SC-ANFIS,此模型对垃圾进料量、空气供给量、氨水投加量和氢氧化钙溶液投加量预测结果的决定系数(R^(2))分别为0.8322、0.9965、0.9957和0.9994,平均绝对百分比误差(MAPE)分别为2.1330%、0.6835%、1.8782%和0.6400%.因此,该模型可应用于生活垃圾焚烧过程控制,提高垃圾焚烧控制精度及自动化程度. 展开更多
关键词 生活垃圾焚烧 神经网络 模糊推理系统 自动控制
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A new methodology for identification of potential pay zones from well logs: Intelligent system establishment and application in the Eastern Junggar Basin, China 被引量:1
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作者 Guo Dali Zhu Kai +2 位作者 Wang Liang Li Jiaqi Xu Jiangwen 《Petroleum Science》 SCIE CAS CSCD 2014年第2期258-264,共7页
In recent years, as the exploration practices extend into more complicated formations, conventional well log interpretation has often shown its inaccuracy and limitations in identifying hydrocarbons. The Permian Wuton... In recent years, as the exploration practices extend into more complicated formations, conventional well log interpretation has often shown its inaccuracy and limitations in identifying hydrocarbons. The Permian Wutonggou Formation hosts typical clastic reservoirs in the Eastern Junggar Basin. The sophisticated lithology characteristics cause complex pore structures and fluid properties. These all finally cause low well testing agreement rate using conventional methods. Eleven years' recent statistics show that 12 out of 15 water layers have been incorrectly identified as being oil or oil/water layers by conventional well log interpretation. This paper proposes a methodology called intelligent prediction and identification system (IPIS). Firstly, parameters reflecting lithological, petrophysical and electrical responses which are greatly related to reservoir fluids have been selected carefully. They are shale content (Vsh), numbered rock type (RN), porosity (φ), permeability (K), true resistivity (RT) and spontaneous-potential (SP). Secondly, Vsh, φ and K are predicted from well logs through artificial neural networks (ANNs). Finally, all the six parameters are input into a neuro-fuzzy inference machine (NFIM) to get fluids identification results. Eighteen new layers of 145.3 m effective thickness were examined by IPIS. There is full agreement with well testing results. This shows the system's accuracy and effectiveness. 展开更多
关键词 Eastern Junggar Basin potential pay zone identification well log interpretation intelligentsystem neural network neuro-fuzzy inference machine
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盾构掘进姿态控制技术研究现状与未来展望 被引量:1
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作者 陈珂 刘天瑞 杨钊 《隧道建设(中英文)》 CSCD 北大核心 2024年第6期1154-1164,共11页
为系统地分析我国盾构掘进姿态控制技术的研究进展,基于知网检索到的32篇相关文献,总结盾构掘进姿态的主要表征参数和影响因素,并以盾构液压推进系统为例论述其控制原理。同时,结合盾构姿态智能控制的部分案例,总结PID控制、自适应控制... 为系统地分析我国盾构掘进姿态控制技术的研究进展,基于知网检索到的32篇相关文献,总结盾构掘进姿态的主要表征参数和影响因素,并以盾构液压推进系统为例论述其控制原理。同时,结合盾构姿态智能控制的部分案例,总结PID控制、自适应控制、模糊控制、基于神经网络的控制和基于智能算法的控制等技术的优劣势及应用场景。基于以上分析,对盾构姿态控制技术的发展方向进行展望。研究发现:1)盾构掘进姿态的影响因素主要包括几何参数、地层参数和盾构掘进参数。2)由于盾构推进系统需要同时完成盾构向前推进和姿态调整等复杂任务,因此该系统的参数对盾构姿态有着很大的影响,是姿态控制的关键因素之一。3)相较于传统PID控制方法,智能控制方法与PID控制的结合可以提高系统的响应速度、精度、适应能力和鲁棒性。4)未来研究可以围绕基于多源数据融合的控制算法、构建数据-机制混合驱动的控制技术以及加强控制技术在实际工程中的实用性等方面展开,实现更精准、更高效的盾构掘进姿态控制。 展开更多
关键词 盾构掘进 姿态控制 PID控制 自适应控制 模糊控制 神经网络 智能算法
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基于EMD-BiLSTM-ANFIS的负荷区间预测 被引量:3
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作者 李宏玉 彭康 +1 位作者 宋来鑫 李桐壮 《吉林大学学报(信息科学版)》 CAS 2024年第1期176-185,共10页
考虑到新型电力负荷随机性增强,传统的准确预测方法已无法满足要求,提出一种EMD-BiLSTM-ANFIS(Empirical Mode Decomposition-Bi-directional Long Short-Term Memory-Adaptive Network-based Fuzzy Inference System)分位数预测负荷概... 考虑到新型电力负荷随机性增强,传统的准确预测方法已无法满足要求,提出一种EMD-BiLSTM-ANFIS(Empirical Mode Decomposition-Bi-directional Long Short-Term Memory-Adaptive Network-based Fuzzy Inference System)分位数预测负荷概率密度的方法,使用负荷预测区间取代点预测的准确数值,能为电力系统分析与决策提供更多数据,增强预测的可靠性。首先将原始负荷序列通过EMD(Empirical Mode Decomposition)分解成若干分量,并通过计算样本熵分为3类分量。然后将重构后的3类分量与由相关性筛选的外界因素特征采用BiLSTM、ANFIS模型进行训练和分位数回归(QR:Quantile Regression),并将分量的预测区间结果累加得到最终负荷的预测区间。最后利用核密度估计输出任意时刻用户负荷概率密度预测结果。通过与CNN-BiLSTM(Convolutional Neural Network-Bidirectional Long Short-Term Memory)、LSTM(Long Short-Term Memory)模型对比点预测及区间预测结果,证明了该方法的有效性。 展开更多
关键词 经验模态分解 双向长短期神经网络 模糊推理系统 分位数回归 概率密度预测
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基于BIM的高速铁路设计概算智能预测方法研究
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作者 段晓晨 高梦婉 +2 位作者 孟阳 孟春成 赵辰光 《铁道运输与经济》 北大核心 2024年第8期136-143,共8页
针对现行高速铁路定额预测存在的固定性、滞后性和预测方法的二维、线性等问题,在分析设计概算和影响因素之间的非线性、不确定性等演变趋势和机理基础上,以已完工程项目的历史数据为基础,构建类似已完工程设计概算历史数据库。为提高... 针对现行高速铁路定额预测存在的固定性、滞后性和预测方法的二维、线性等问题,在分析设计概算和影响因素之间的非线性、不确定性等演变趋势和机理基础上,以已完工程项目的历史数据为基础,构建类似已完工程设计概算历史数据库。为提高智能预测的精确度,采用余弦相似度方法在数据库中筛选相似案例,对拟建高速铁路项目进行类似度分类,采用非线性反向传播神经网络、模糊C均值聚类、模糊推理等方法集成优化组合,构建高速铁路拟建工程设计概算智能预测模型和BIM三维可视化模型。研究结果表明,建立设计概算非线性集成方法预测模型,实现不同量级数据下预测方法的优势互补,保证预测精度;通过BIM技术建立的三维可视化模型,有效提升设计概算预测的智能化水平与可视化效果。 展开更多
关键词 高速铁路 设计概算 反向传播神经网络 模糊C均值聚类 模糊推理 BIM 预测
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基于BSO改进模糊神经网络PID的管道压力控制策略研究 被引量:3
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作者 张项飞 李敬兆 刘泽朝 《煤矿机械》 2024年第9期167-170,共4页
针对煤矿供水管道压力控制系统智能化程度低、人工操作实时性和准确性差等问题,设计了一种基于天牛群优化(BSO)改进模糊神经网络PID的管道压力控制策略。首先,基于BSO算法对PID控制器的初始参数进行参数迭代优化,找到最佳初始参数;其次... 针对煤矿供水管道压力控制系统智能化程度低、人工操作实时性和准确性差等问题,设计了一种基于天牛群优化(BSO)改进模糊神经网络PID的管道压力控制策略。首先,基于BSO算法对PID控制器的初始参数进行参数迭代优化,找到最佳初始参数;其次,通过模糊神经网络实现实际压力偏差值的模糊化、模糊推理等处理,实时调整PID控制参数;最后,PID控制器运算后得到的输出信号作用在执行机构上,实现电动阀门的智能化控制。实验结果表明,该控制策略响应速度更快,超调量更小,稳定性更强,满足阀门控制开度要求,提高了煤矿井下供水系统的智能化水平,达到了减人、降本增效的目的。 展开更多
关键词 BSO 模糊神经网络 PID 模糊推理
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基于ANFIS-LSSVM的计算颜色恒常性算法研究
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作者 王兴光 罗运辉 +1 位作者 王庆 陈业红 《齐鲁工业大学学报》 CAS 2024年第2期62-72,共11页
计算颜色恒常性是指消除场景光源的影响从而再现物体真实颜色的能力。目前,深度神经网络的应用使颜色恒常性精度显著提高,但大多数深度学习算法训练时间长、计算复杂度高,且需要大量的训练样本。针对此问题,提出了一种结合自适应神经模... 计算颜色恒常性是指消除场景光源的影响从而再现物体真实颜色的能力。目前,深度神经网络的应用使颜色恒常性精度显著提高,但大多数深度学习算法训练时间长、计算复杂度高,且需要大量的训练样本。针对此问题,提出了一种结合自适应神经模糊推理系统(ANFIS)和最小二乘支持向量机(LSSVM)的简单有效的方法。该方法分为训练和预测两个阶段:在训练阶段,首先提取图像特征分别训练ANFIS、LSSVM两种初始光源估计模型,接着利用核函数变换将两种模型融合,然后利用预留训练样本进一步训练得到多元线性回归光源估计模型;在预测阶段,提取测试图像特征后,直接由训练所得模型预测得到该测试图像最终的场景光源颜色值。实验结果表明,与深度学习方法相比,本文所提方法计算复杂度较低,即使在小训练样本中也能有很好的光源估计性能。 展开更多
关键词 计算颜色恒常性 光源估计 自适应神经模糊推理系统(ANFIS) 最小二乘支持向量机(LSSVM)
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基于模糊自适应RBF的机械臂积分滑模控制方法
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作者 冯嘉庆 张蕾 田冬雨 《西北工业大学学报》 EI CAS CSCD 北大核心 2024年第6期1099-1110,共12页
针对机械臂动力学模型参数具有不确定性,系统控制精度和收敛速度受到关节摩擦和外部干扰影响的问题,提出一种基于机械臂动力学模型的复合控制策略。结合改进型双幂次趋近律和积分滑模设计滑模控制项,加快跟踪误差的收敛速度;通过3组RBF... 针对机械臂动力学模型参数具有不确定性,系统控制精度和收敛速度受到关节摩擦和外部干扰影响的问题,提出一种基于机械臂动力学模型的复合控制策略。结合改进型双幂次趋近律和积分滑模设计滑模控制项,加快跟踪误差的收敛速度;通过3组RBF神经网络分别逼近动力学模型的不确定参数,引入自适应机制对权值进行在线的自适应整定,并采用前述设计的滑模控制项补偿RBF神经网络的逼近误差;利用模糊控制器对关节摩擦和外部干扰进行补偿。仿真结果表明,与基于分块RBF神经网络逼近滑模控制算法相比,所提出的复合控制策略使机械臂关节角速度响应时间缩减39.4%,最大稳态误差缩减76.8%,平均稳态误差缩减62.7%,机械臂关节空间轨迹跟踪的控制精度和响应速度得到显著提高。 展开更多
关键词 机械臂 轨迹跟踪 自适应RBF神经网络 模糊补偿 积分滑模
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A novel cascaded H-bridge photovoltaic inverter with flexible arc suppression function
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作者 Junyi Tang Wei Gao 《Global Energy Interconnection》 EI CSCD 2024年第4期513-527,共15页
This paper presents a novel approach that simultaneously enables photovoltaic(PV)inversion and flexible arc suppression during single-phase grounding faults.Inverters compensate for ground currents through an arc-elim... This paper presents a novel approach that simultaneously enables photovoltaic(PV)inversion and flexible arc suppression during single-phase grounding faults.Inverters compensate for ground currents through an arc-elimination function,while outputting a PV direct current(DC)power supply.This method effectively reduces the residual grounding current.To reduce the dependence of the arc-suppression performance on accurate compensation current-injection models,an adaptive fuzzy neural network imitating a sliding mode controller was designed.An online adaptive adjustment law for network parameters was developed,based on the Lyapunov stability theorem,to improve the robustness of the inverter to fault and connection locations.Furthermore,a new arc-suppression control exit strategy is proposed to allow a zerosequence voltage amplitude to quickly and smoothly track a target value by controlling the nonlinear decrease in current and reducing the regulation time.Simulation results showed that the proposed method can effectively achieve fast arc suppression and reduce the fault impact current in single-phase grounding faults.Compared to other methods,the proposed method can generate a lower residual grounding current and maintain good arc-suppression performance under different transition resistances and fault locations. 展开更多
关键词 Photovoltaic inverter Flexible arc suppression adaptive control fuzzy neural network Sliding mode control Exit strategy
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基于改进FCM-LSTM的光伏出力短期预测研究
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作者 秦宇 许野 +2 位作者 王鑫鹏 王涛 李薇 《太阳能学报》 EI CAS CSCD 北大核心 2024年第8期304-313,共10页
受制于外界气象条件和设备性能损失等多方面因素的影响,光伏电站的发电功率呈现出很强的波动性和随机性,精确的光伏出力预测对光伏电站的运营管理和电网的调度运行至关重要。针对传统模糊C均值聚类算法(FCM)无法自主确定聚类数以及欧氏... 受制于外界气象条件和设备性能损失等多方面因素的影响,光伏电站的发电功率呈现出很强的波动性和随机性,精确的光伏出力预测对光伏电站的运营管理和电网的调度运行至关重要。针对传统模糊C均值聚类算法(FCM)无法自主确定聚类数以及欧氏距离在高维数据分类上的不足,在传统FCM的基础上引入自适应因子和加入余弦距离作为样本分类指标,确定与待预测数据相似程度最高的历史样本簇集,创新性地提出一种基于改进FCM和长短期记忆(LSTM)神经网络的短期光伏出力组合预测模型。在云南某光伏电站的应用结果显示,对比其他预测模型,所提方法的历史样本分类效果更佳,发电功率预测精度更高,验证了该方法的有效性与优越性。 展开更多
关键词 光伏出力短期预测 模糊C均值聚类 自适应方法 余弦距离 长短期记忆神经网络
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电动机变频控制中的智能调速算法分析
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作者 向炜 《集成电路应用》 2024年第11期19-21,共3页
阐述一种基于智能算法的变频控制策略。该算法利用模糊逻辑和神经网络技术,实现对电动机的自适应调速控制,提高系统的响应速度和稳定性。该算法通过模糊规则和神经网络的在线学习,根据负载变化和设定值自动调整输出频率。仿真结果表明,... 阐述一种基于智能算法的变频控制策略。该算法利用模糊逻辑和神经网络技术,实现对电动机的自适应调速控制,提高系统的响应速度和稳定性。该算法通过模糊规则和神经网络的在线学习,根据负载变化和设定值自动调整输出频率。仿真结果表明,该算法具有良好的动态性能和鲁棒性。 展开更多
关键词 变频控制 智能算法 模糊逻辑 神经网络 自适应调速
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