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An Autonomous Incremental Learning Algorithm for Radial Basis Function Networks
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作者 Seiichi Ozawa Toshihisa Tabuchi +1 位作者 Sho Nakasaka Asim Roy 《Journal of Intelligent Learning Systems and Applications》 2010年第4期179-189,共11页
In this paper, an incremental learning model called Resource Allocating Network with Long-Term Memory (RAN-LTM) is extended such that the learning is conducted with some autonomy for the following functions: 1) data c... In this paper, an incremental learning model called Resource Allocating Network with Long-Term Memory (RAN-LTM) is extended such that the learning is conducted with some autonomy for the following functions: 1) data collection for initial learning, 2) data normalization, 3) addition of radial basis functions (RBFs), and 4) determination of RBF cen-ters and widths. The proposed learning algorithm called Autonomous Learning algorithm for Resource Allocating Network (AL-RAN) is divided into the two learning phases: initial learning phase and incremental learning phase. And the former is further divided into the autonomous data collection and the initial network learning. In the initial learning phase, training data are first collected until the class separability is converged or has a significant dif-ference between normalized and unnormalized data. Then, an initial structure of AL-RAN is autonomously determined by selecting a moderate number of RBF centers from the collected data and by defining as large RBF widths as possible within a proper range. After the initial learning, the incremental learning of AL-RAN is conducted in a sequential way whenever a new training data is given. In the experiments, we evaluate AL-RAN using five benchmark data sets. From the experimental results, we confirm that the above autonomous functions work well and the efficiency in terms of network structure and learning time is improved without sacrificing the recognition accuracy as compared with the previous version of AL-RAN. 展开更多
关键词 AUTONOMOUS learning INCREMENTAL learning RADIAL basis function Network PATTERN Recognition
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New Structural Self-Organizing Fuzzy CMAC with Basis Functions
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作者 何超 徐立新 +1 位作者 董宁 张宇河 《Journal of Beijing Institute of Technology》 EI CAS 2001年第3期298-305,共8页
To improve the nonlinear approximating ability of cerebellar model articulation controller(CMAC), by introducing the Gauss basis functions and the similarity measure based addressing scheme, a new kind of fuzzy CMAC... To improve the nonlinear approximating ability of cerebellar model articulation controller(CMAC), by introducing the Gauss basis functions and the similarity measure based addressing scheme, a new kind of fuzzy CMAC with Gauss basis functions(GFCMAC) was presented. Moreover, based upon the improvement of the self organizing feature map algorithm of Kohonen, the structural self organizing algorithm for GFCMAC(SOGFCMAC) was proposed. Simulation results show that adopting the Gauss basis functions and fuzzy techniques can remarkably improve the nonlinear approximating capacity of CMAC. Compared with the traditional CMAC,CMAC with general basis functions and fuzzy CMAC(FCMAC), SOGFCMAC has the obvious advantages in the aspects of the convergent speed, approximating accuracy and structural self organizing. 展开更多
关键词 cmac FUZZY basis functions self organizing algorithm neural networks
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小脑模型关节控制器(CMAC)理论及应用 被引量:17
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作者 苏刚 陈增强 袁著祉 《仪器仪表学报》 EI CAS CSCD 北大核心 2003年第z1期269-273,共5页
CMAC神经网络因具有收敛速度快、泛化能力强、结构简单、易于软、硬件实现等特点 ,而得到广泛的应用。本文系统地综述了 CMAC神经网络的结构、算法、收敛性以及在控制中的应用。指出
关键词 cmac 神经网络 学习算法 收敛性 建模 控制
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基于广义基函数的CMAC学习算法的改进及收敛性分析 被引量:12
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作者 段培永 邵惠鹤 《自动化学报》 EI CSCD 北大核心 1999年第2期258-263,共6页
基于广义基函数的CMAC(CerebelarModelArticulationControler)学习算法(称C-L算法)收敛条件依赖于基函数和学习样本,很难同时满足学习快速性与收敛性.提出了一种改进学习算法,并证明... 基于广义基函数的CMAC(CerebelarModelArticulationControler)学习算法(称C-L算法)收敛条件依赖于基函数和学习样本,很难同时满足学习快速性与收敛性.提出了一种改进学习算法,并证明改进算法是收敛的,而且收敛条件不依赖于基函数和学习样本.仿真结果表明改进算法优于C-L算法和标准的Albus算法. 展开更多
关键词 cmac学习算法 广义基函数 收敛性 神经网络
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高阶CMAC神经网络的研究 被引量:3
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作者 丁国锋 王孙安 +1 位作者 林廷圻 史维祥 《信息与控制》 CSCD 北大核心 1996年第6期373-380,共8页
提出了一种高阶CMAC(HCMAC)神经网络.它是采用高阶的径向基函数作为接收域函数,为了进一步增强对输入模式的表达,还可以用接收域函数与输入模式向量构成张量积,这时产生的是高维的增强表达,同时HCMAC沿用CMAC... 提出了一种高阶CMAC(HCMAC)神经网络.它是采用高阶的径向基函数作为接收域函数,为了进一步增强对输入模式的表达,还可以用接收域函数与输入模式向量构成张量积,这时产生的是高维的增强表达,同时HCMAC沿用CMAC的地址映射方法.由于高阶接收域函数的引入,使其可以获得较CMAC连续性强且有解析微分的复杂函数近似.HCMAC在不改变CMAC简单结构的基础上较RBF网络有计算量少,学习效率高等优点.文中还首次将用于参数估计的Kalman滤波学习算法引入到这种类CMAC的网络学习中,这使HCMAC有更高的学习速度.通过仿真研究表明HCMAC除拥有CMAC和RBF网络两者的优点外。 展开更多
关键词 cmac网络 径向基函数 神经网络
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模糊CMAC神经网络控制系统及混合学习算法 被引量:5
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作者 程启明 王勇浩 《电机与控制学报》 EI CSCD 北大核心 2006年第2期216-221,共6页
针对CMAC神经网络和模糊控制的特性,给出了一种能反映人脑认知的模糊性和连续性的模糊CMAC神经网络控制器,该控制器采用高斯函数作为模糊隶属函数,利用神经网络实现模糊推理并可对隶属函数进行实时调整,从而使其具有学习和自适应能力。... 针对CMAC神经网络和模糊控制的特性,给出了一种能反映人脑认知的模糊性和连续性的模糊CMAC神经网络控制器,该控制器采用高斯函数作为模糊隶属函数,利用神经网络实现模糊推理并可对隶属函数进行实时调整,从而使其具有学习和自适应能力。讨论了这种控制器的混合学习算法,即先采用混沌算法离线优化,再采用BP梯度算法在线调整,并推导了变形Elmam网络的系统辨识算法。对电厂锅炉主蒸汽温度控制的仿真结果表明了此控制器及其学习算法的可行性和有效性。 展开更多
关键词 模糊cmac神经网络 混合学习算法 混沌优化算法 变形Elmam网络 主汽温
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CMAC 的一种快速学习方法 被引量:2
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作者 张俊杰 杨艳丽 +1 位作者 尤昌德 王雅丽 《西安工业学院学报》 CAS 1997年第2期98-103,共6页
研究了小脑模型连接控制器(CMAC)的快速学习方法.首先分析了学习过程中学习干扰的原因及学习精度、学习次数、内存单元数之间的关系,然后基于内存单元的初始化和学习样本点的选择,构造了可快速精确地收敛于学习函数的快速学习... 研究了小脑模型连接控制器(CMAC)的快速学习方法.首先分析了学习过程中学习干扰的原因及学习精度、学习次数、内存单元数之间的关系,然后基于内存单元的初始化和学习样本点的选择,构造了可快速精确地收敛于学习函数的快速学习方法———初始化随机法. 展开更多
关键词 神经网络 cmac 快速学习方法 小脑型控制器
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高斯基函数CMAC神经网络用于克服摩擦非线性的研究 被引量:2
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作者 蒋志明 吴伟 林廷圻 《机床与液压》 北大核心 2000年第3期24-25,共2页
研究了一种高斯基函数CMAC神经网络用于克服液压伺服系统中摩擦非线性的控制器设计问题。该控制器不需要对摩擦力进行建模 ,而是采用学习的方法克服摩擦非线性 ,因此具有通用性。仿真结果表明 ,该种控制器不仅是有效的 。
关键词 摩擦 cmac神经网络 高斯基函数 学习控制 非线性
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高超声速飞行器的模糊CMAC神经网络控制器设计 被引量:1
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作者 闫斌斌 鹿存侃 闫杰 《计算机测量与控制》 CSCD 北大核心 2009年第11期2226-2228,共3页
由于采用机体一体化设计,吸气式高超声速飞行器的气动特性难以准确获知,建立的数学模型是极不准确的;设计了一种模糊CMAC神经网络(FCMAC)控制器及其学习算法,在CMAC神经网络控制器中结合模糊逻辑理论,使得CMAC控制器具有自学习能力;仿... 由于采用机体一体化设计,吸气式高超声速飞行器的气动特性难以准确获知,建立的数学模型是极不准确的;设计了一种模糊CMAC神经网络(FCMAC)控制器及其学习算法,在CMAC神经网络控制器中结合模糊逻辑理论,使得CMAC控制器具有自学习能力;仿真用高超声速飞行器的纵向模型对该控制器进行了验证,证明该控制方法能够有效地跟踪飞行器的高度和速度指令。 展开更多
关键词 高超声速飞行器 cmac神经网络 学习算法 模糊控制机
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GBF-CMAC和滑模控制的柔性结构系统控制 被引量:2
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作者 付兴建 于士贤 《智能系统学报》 CSCD 北大核心 2018年第5期791-798,共8页
针对一类不确定系统的跟踪控制,设计了一种将GBF-CMAC(cerebellar model articulation controller with Gauss basis function)与滑模控制相结合的控制系统。利用符号距离和分层结构减少了神经网络所需存储器的数量,并提出了一种神经网... 针对一类不确定系统的跟踪控制,设计了一种将GBF-CMAC(cerebellar model articulation controller with Gauss basis function)与滑模控制相结合的控制系统。利用符号距离和分层结构减少了神经网络所需存储器的数量,并提出了一种神经网络参数的自适应学习律。将设计的控制器用于含有不确定性和欠驱动结构的高阶柔性直线结构系统的跟踪控制,并与一般滑模控制和积分滑模控制进行了比较。实验结果表明,所设计的控制器不仅具有较好的鲁棒性,而且改善了滑模控制存在的抖振问题。同时通过调整神经网络的参数对抖振进行控制,实现了抖振和跟踪性能之间的最优选择。 展开更多
关键词 高斯基函数 小脑模型控制器 神经网络 自适应 分层结构 滑模控制 不确定系统 柔性直线系统
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伺服系统的高斯基函数CMAC学习控制研究
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作者 齐海龙 李秀娟 《自动化技术与应用》 2004年第4期20-23,共4页
在高精度伺服系统中 ,由于摩擦力及负载扰动等因素的影响 ,常规PID控制难以满足越来越高的控制精度、跟踪性能等指标要求。本文提出了基于高斯基函数CMAC在线学习的控制方案 ,并给出了相应的学习算法。仿真结果表明 ,该方法不仅有较好... 在高精度伺服系统中 ,由于摩擦力及负载扰动等因素的影响 ,常规PID控制难以满足越来越高的控制精度、跟踪性能等指标要求。本文提出了基于高斯基函数CMAC在线学习的控制方案 ,并给出了相应的学习算法。仿真结果表明 ,该方法不仅有较好的控制精度 。 展开更多
关键词 伺服系统 cmac 高斯基 学习控制
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高斯基函数CMAC快速算法的改进及应用研究 被引量:2
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作者 齐海龙 李秀娟 《南京理工大学学报》 EI CAS CSCD 北大核心 2005年第2期140-143,共4页
该文对基于高斯基函数小脑模型(CMAC)的快速算法进行了改进,针对其学习速率的选取问题,提出了一种基于遗传算法的学习速率最优选取方法,使得CMAC学习速率的选取得到了最优化。讨论了该算法的实际可行性,提出了参数选择和实时控制相分离... 该文对基于高斯基函数小脑模型(CMAC)的快速算法进行了改进,针对其学习速率的选取问题,提出了一种基于遗传算法的学习速率最优选取方法,使得CMAC学习速率的选取得到了最优化。讨论了该算法的实际可行性,提出了参数选择和实时控制相分离的策略,并在某转台伺服系统模型中进行了应用研究。仿真结果表明,改进算法避免了学习速率选取的经验不确定性。 展开更多
关键词 小脑模型 高斯基函数 遗传算法 学习速率 伺服系统
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基于TSK模糊系统的改进CMAC超闭球网络结构 被引量:1
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作者 许斌 王士同 Wang Shi-Tong 《华东船舶工业学院学报》 EI 2001年第2期53-57,共5页
充分利用了TSK(Sugeno Tanaka)模糊系统的优点 ,提出了改进的CMAC(CerebellarModelAr ticulationController)超闭球结构网络 ,给出了其学习算法 ,实验结果表明改进的CMAC超闭球结构网络较之CMAC超闭球结构网络对样本有更高的逼近精度。
关键词 超闭球网络结构 学习算法 模糊系统 小脑模型神经网络
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基于改进CMAC的高阶柔性直线系统跟踪控制 被引量:4
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作者 于士贤 付兴建 《北京信息科技大学学报(自然科学版)》 2018年第1期18-22,共5页
针对高阶柔性直线系统的跟踪控制问题,设计了一种将改进的小脑模型关节控制器(Cerebellar Model Articulation Controller,CMAC)神经网络与传统控制器并联的控制方案。基于参数自适应算法和梯度下降法提出了一种高斯基函数CMAC神经网络... 针对高阶柔性直线系统的跟踪控制问题,设计了一种将改进的小脑模型关节控制器(Cerebellar Model Articulation Controller,CMAC)神经网络与传统控制器并联的控制方案。基于参数自适应算法和梯度下降法提出了一种高斯基函数CMAC神经网络的参数自适应更新律;利用符号距离的概念减少了神经网络的输入维数;并利用自适应遗传算法对神经网络的学习率进行了优化。仿真实验表明改进的高斯基函数CMAC具有比传统CMAC更好的学习能力,控制方案实现了高阶柔性直线系统的无差跟踪控制。 展开更多
关键词 小脑模型关节控制器 高斯基函数 自适应遗传算法 高阶柔性直线系统
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A Simple Hybrid Recursive Learning Algorithm with High Generalization Performance for Radial Basis Function Neural Network 被引量:12
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作者 ZHU Tao,\ WANG Zheng\|ou Institute of Systems Engineering, Tianjin University, Tianjin 300072, China 《Systems Science and Systems Engineering》 CSCD 2000年第1期16-27,共12页
In this paper, we propose a simple learning algorithm for non\|linear function approximation and system modeling using minimal radial basis function neural network with high generalization performance. A hybrid algori... In this paper, we propose a simple learning algorithm for non\|linear function approximation and system modeling using minimal radial basis function neural network with high generalization performance. A hybrid algorithm is constructed, which combines recursive n \|means clustering algorithm with a simple recursive regularized least squares algorithm (SRRLS). The n \|means clustering algorithm adjusts the centers of the network, while the SRRLS constructs a parsimonious network which makes the generalization performance of the network well. The SRRLS algorithm needs no matrix computing, so it has a lower computational cost and no ill\|conditional problem. Because of the recursive manner, this algorithm is suitable for on\|line applications. The effectiveness of this algorithm is demonstrated by two benchmark examples. 展开更多
关键词 radial basis function neural network GENERALIZATION regularized least squares SIMPLICITY n\| means clustering recursive algorithm
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DETERMINING THE STRUCTURES AND PARAMETERS OF RADIAL BASIS FUNCTION NEURAL NETWORKS USING IMPROVED GENETIC ALGORITHMS 被引量:1
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作者 Meiqin Liu Jida Chen 《Journal of Central South University》 SCIE EI CAS 1998年第2期68-73,共6页
The method of determining the structures and parameters of radial basis function neural networks(RBFNNs) using improved genetic algorithms is proposed. Akaike′s information criterion (AIC) with generalization error t... The method of determining the structures and parameters of radial basis function neural networks(RBFNNs) using improved genetic algorithms is proposed. Akaike′s information criterion (AIC) with generalization error term is used as the best criterion of optimizing the structures and parameters of networks. It is shown from the simulation results that the method not only improves the approximation and generalization capability of RBFNNs ,but also obtain the optimal or suboptimal structures of networks. 展开更多
关键词 RADIAL basis function NEURAL network GENETIC algorithms Akaike′s information CRITERION OVERFITTING
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A new sequential learning algorithm for RBF neural networks 被引量:5
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作者 YANG Ge1, LV Jianhong1 & LIU Zhiyuan2 1. Department of Power Engineering, Southeast University, Nanjing 210096, China 2. Department of Power Engineering, Nanjing Institute of Technology, Nanjing 210013, China 《Science China(Technological Sciences)》 SCIE EI CAS 2004年第4期447-460,共14页
Due to their inherent imperfections, it is hard to use the static neural networks for nonlinear time-varying process modeling and prediction, and the minimal resource allocation network (MRAN) is difficult to be reali... Due to their inherent imperfections, it is hard to use the static neural networks for nonlinear time-varying process modeling and prediction, and the minimal resource allocation network (MRAN) is difficult to be realized for its too many regulation parameters. A new sequential learning algorithm for radial basis function (RBF) neural networks based on local projection named Local Projection Network (LPN) is proposed in this paper. The results of validation for several benchmark problems with the new algorithm show that the presented LPN not only has the same level as M-RAN in network size and precision of the outputs, but also has fewer regulation parameters and is more predictable. 展开更多
关键词 RADIAL basis functions local PROJECTION network MINIMAL resource ALLOCATION network learning algorithm.
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Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms
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作者 Shehab Abdulhabib Alzaeemi Kim Gaik Tay +2 位作者 Audrey Huong Saratha Sathasivam Majid Khan bin Majahar Ali 《Computer Systems Science & Engineering》 SCIE EI 2023年第10期1163-1184,共22页
Radial Basis Function Neural Network(RBFNN)ensembles have long suffered from non-efficient training,where incorrect parameter settings can be computationally disastrous.This paper examines different evolutionary algor... Radial Basis Function Neural Network(RBFNN)ensembles have long suffered from non-efficient training,where incorrect parameter settings can be computationally disastrous.This paper examines different evolutionary algorithms for training the Symbolic Radial Basis Function Neural Network(SRBFNN)through the behavior’s integration of satisfiability programming.Inspired by evolutionary algorithms,which can iteratively find the nearoptimal solution,different Evolutionary Algorithms(EAs)were designed to optimize the producer output weight of the SRBFNN that corresponds to the embedded logic programming 2Satisfiability representation(SRBFNN-2SAT).The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms,including Genetic Algorithm(GA),Evolution Strategy Algorithm(ES),Differential Evolution Algorithm(DE),and Evolutionary Programming Algorithm(EP).Each of these methods is presented in the steps in the flowchart form which can be used for its straightforward implementation in any programming language.With the use of SRBFNN-2SAT,a training method based on these algorithms has been presented,then training has been compared among algorithms,which were applied in Microsoft Visual C++software using multiple metrics of performance,including Mean Absolute Relative Error(MARE),Root Mean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),Mean Bias Error(MBE),Systematic Error(SD),Schwarz Bayesian Criterion(SBC),and Central Process Unit time(CPU time).Based on the results,the EP algorithm achieved a higher training rate and simple structure compared with the rest of the algorithms.It has been confirmed that the EP algorithm is quite effective in training and obtaining the best output weight,accompanied by the slightest iteration error,which minimizes the objective function of SRBFNN-2SAT. 展开更多
关键词 Satisfiability logic programming symbolic radial basis function neural network evolutionary programming algorithm genetic algorithm evolution strategy algorithm differential evolution algorithm
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Application of Radial Basis Function Network in Sensor Failure Detection
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作者 钮永胜 赵新民 《Journal of Beijing Institute of Technology》 EI CAS 1999年第2期70-76,共7页
Aim To detect sensor failure in control system using a single sensor signal. Methods A neural predictor was designed based on a radial basis function network(RBFN), and the neural predictor learned the sensor sig... Aim To detect sensor failure in control system using a single sensor signal. Methods A neural predictor was designed based on a radial basis function network(RBFN), and the neural predictor learned the sensor signal on line with a hybrid algorithm composed of n means clustering and Kalman filter and then gave the estimation of the sensor signal at the next step. If the difference between the estimation and the actural values of the sensor signal exceeded a threshold, the sensor could be declared to have a failure. The choice of the failure detection threshold depends on the noise variance and the possible prediction error of neural predictor. Results and Conclusion\ The computer simulation results show the proposed method can detect sensor failure correctly for a gyro in an automotive engine. 展开更多
关键词 sensor failure failure detection radial basis function network(BRFN) on line learning
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Crack Fault Diagnosis and Location Method for a Dual-Disk Hollow Shaft Rotor System Based on the Radial Basis Function Network and Pattern Recognition Neural Network 被引量:2
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作者 Yuhong Jin Lei Hou +1 位作者 Zhenyong Lu Yushu Chen 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2023年第2期180-197,共18页
The crack fault is one of the most common faults in the rotor system,and researchers have paid close attention to its fault diagnosis.However,most studies focus on discussing the dynamic response characteristics cause... The crack fault is one of the most common faults in the rotor system,and researchers have paid close attention to its fault diagnosis.However,most studies focus on discussing the dynamic response characteristics caused by the crack rather than estimating the crack depth and position based on the obtained vibration signals.In this paper,a novel crack fault diagnosis and location method for a dual-disk hollow shaft rotor system based on the Radial basis function(RBF)network and Pattern recognition neural network(PRNN)is presented.Firstly,a rotor system model with a breathing crack suitable for a short-thick hollow shaft rotor is established based on the finite element method,where the crack's periodic opening and closing pattern and different degrees of crack depth are considered.Then,the dynamic response is obtained by the harmonic balance method.By adjusting the crack parameters,the dynamic characteristics related to the crack depth and position are analyzed through the amplitude-frequency responses and waterfall plots.The analysis results show that the first critical speed,first subcritical speed,first critical speed amplitude,and super-harmonic resonance peak at the first subcritical speed can be utilized for the crack fault diagnosis.Based on this,the RBF network and PRNN are adopted to determine the depth and approximate location of the crack respectively by taking the above dynamic characteristics as input.Test results show that the proposed method has high fault diagnosis accuracy.This research proposes a crack detection method adequate for the hollow shaft rotor system,where the crack depth and position are both unknown. 展开更多
关键词 Hollow shaft rotor Breathing crack Radial basis function network Pattern recognition neural network Machine learning
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