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Kosmatopoulos E.B. Polycarpou M.M. Christodoulou M.A. Ioannou P.A. 《Neural Networks, IEEE Transactions on》1995,6(2):422-431
Several continuous-time and discrete-time recurrent neural network models have been developed and applied to various engineering problems. One of the difficulties encountered in the application of recurrent networks is the derivation of efficient learning algorithms that also guarantee the stability of the overall system. This paper studies the approximation and learning properties of one class of recurrent networks, known as high-order neural networks; and applies these architectures to the identification of dynamical systems. In recurrent high-order neural networks, the dynamic components are distributed throughout the network in the form of dynamic neurons. It is shown that if enough high-order connections are allowed then this network is capable of approximating arbitrary dynamical systems. Identification schemes based on high-order network architectures are designed and analyzed. 相似文献
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一类基于神经网络非线性观测器的鲁棒故障检测 总被引:3,自引:0,他引:3
针对一类仿射非线性动态系统,提出了一种基
于神经网络非线性观测器的鲁棒故障检测与隔离的新方法.该方法采用神经网络逼近观测器
系统中的非线性项,提高了状态估计的精度,并从理论上证明了状态估计误差稳定且渐近收
敛到零;另一方面引入神经网络分类器进行故障的模式识别,通过在神经网络输入端加入噪
声项来进行训练,提高神经网络的泛化逼近能力,从而保证对被监测系统的建模误差和外部
扰动具有良好的鲁棒性.最后,利用本文方法针对某型歼击机结构故障进行仿真验证,仿真
结果表明本文方法是有效的. 相似文献
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The paper deals with problems of fault detection of industrial processes using dynamic neural networks. The considered neural network has a feed-forward multi-layer structure and dynamic characteristics are obtained by using dynamic neuron models. Two optimisation problems are associated with neural networks. The first one is selection of a proper network structure which is solved by using information criteria such as the Akaike Information Criterion or the Final Prediction Error. In turn, the training of the network is performed by a stochastic approximation algorithm. The effectiveness of the proposed fault detection and isolation system is checked using real data recorded in Lublin Sugar Factory, Poland. Additionally, a comparison with alternative approaches is presented. 相似文献
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执行机构与敏感器故障检测与定位是深空探测任务卫星平台可靠运行的前提和保障.本文从数据的角度出发,结合姿控系统工作机理,提出一种基于神经网络和支持向量机结合的故障诊断方法用于检测并定位故障.故障诊断方法分为3步,首先采集姿控系统的状态信息,采用神经网络对闭环姿控系统中未知动态特性建模并进行预测;然后将姿控系统敏感器信号与神经网络预测输出比较生成残差并提取故障特征;最后采用支持向量机辨识残差特征检测故障,并结合运动学特性分析定位故障.仿真结果表明本文所提方法可以有效提取、辨识故障特征,实现执行器与敏感器的故障检测定位. 相似文献
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一种基于小波神经网络故障检测方法的仿真研究 总被引:5,自引:1,他引:4
文中提出了一种基于小波神经网络一性观测器的故障检测方法。它是一种把信号分析和模型相结合的故障检测方法,通过小波对信号的去噪和神经的神经网络的自学习功能,来获取系统输入输出的非线性动力学特性,进而实时计算出残差并进行逻辑判疡,可提高故障检测的速度和准确率。对同步交流电机的结构损伤故障进行了仿真,结果表明了该方法是可行的。 相似文献
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针对合有高阶不确定扰动项且不可参数线性化的一类非线性系统,采用反步递推方法设计基于多层神经网络的自适应控制器,多层神经网络可较好地逼近非线性系统,其权值能在系统先验知识不多的情况下在线调整,给出了神经网络Lyapunov意义下稳定的在线自适应律,在设计控制器的过程中,采用类加权形式Lyapunov函数,使得控制器能有效处理自适应控制奇异性问题,仿真结果表明,该控制器对系统参数的不确定性和有界干扰具有一定的鲁棒性,并能保证闭环系统全局稳定。 相似文献
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在软件开发的早期预测有失效倾向的软件模块,能够极大地提高软件的质量.软件失效预测中的一个普遍问题是数据中噪声的存在.神经网络具有鲁棒性而且对噪声有很强的抑制能力.不同结构的神经网络在训练算法和应用领域都有差异.该文主要就软件失效预测这个应用领域叙述几种适用的网络,并比较这几种网络在训练结果和性能上的差异.上述方法在SDH通信软件的失效预测中得到了成功的应用.试验结果显示虽然MLP、PNN、LVQ网络都能解决这类模式分类问题,但是只有MLP网络训练结果比较稳定,在不同的数据集上训练出的网络都有很好的预测效果. 相似文献
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One of the research problems investigated these days is early fault detection. To this end, advanced signal processing algorithms are employed. The present paper makes an attempt at early fault detection in a gearbox. In order to evaluate its technical condition, artificial neural networks were used. Early fault detection based on support vector machines is a relatively new and rarely employed method for evaluating condition of machines, particularly gearboxes. The available literature offers very promising results of using this method. In order to compare the obtained results, a multilayer perceptron network was created. Such standard neural network ensures high effectiveness. The vibration signal obtained from a sensor is seldom a material for direct analysis. First, it needs to be processed to bring out the informative part of the signal. To this end, a wavelet transform was used. The presented results concern both a “raw” vibration signal and processed one, investigated for two neural networks. The wavelet transform has proved to improve significantly the accuracy of condition evaluation and the results obtained by the two networks are consistent with one another. 相似文献
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A neural network model for fault detection in conjunction with a programmable logic controller 总被引:2,自引:0,他引:2
This paper discusses the feasibility of using neural networks as a tool in the fault detection process. A neural network is integrated with a state language programmable logic controller, an important device in an automatic control system. Time series data related to time spent in a state is gathered and used as input into a neural network, for the purpose of identifying when a fault has occurred. A feedforward neural network is used to identify which (if any) of three types of faults may have occurred. Experimental results related to sensitivity and accuracy measures are presented. A brief review of related applications and research is also presented. 相似文献
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不确定非线性系统的自适应反推高阶终端滑模控制 总被引:1,自引:0,他引:1
针对一类非匹配不确定非线性系统,提出一种神经网络自适应反推高阶终端滑模控制方案.反推设计的前1步利用神经网络逼近未知非线性函数,结合动态面控制设计虚拟控制律,避免传统反推设计存在的计算复杂性问题,并抑制非匹配不确定性的影响;第步结合非奇异终端滑模设计高阶滑模控制律,去除控制抖振,使系统对于匹配和非匹配不确定性均具有鲁棒性.理论分析证明了闭环系统状态半全局一致终结有界,仿真结果表明了所提出方法的有效性. 相似文献
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Fuzzy neural network with general parameter adaptation for modelingof nonlinear time-series 总被引:5,自引:0,他引:5
By taking advantage of fuzzy systems and neural networks, a fuzzy-neural network with a general parameter (GP) learning algorithm and heuristic model structure determination is proposed in this paper. Our network model is based on the Gaussian radial basis function network (RBFN). We use the flexible GP approach both for initializing the off-line training algorithm and fine-tuning the nonlinear model efficiently in online operation. A modification of the robust unbiasedness criterion using distorter (UCD) is utilized for selecting the structural parameters of this adaptive model. The UCD approach provides the desired modeling accuracy and avoids the risk of over-fitting. In order to illustrate the operation of the proposed modeling scheme, it is experimentally applied to a fault detection application. 相似文献
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Classical signal processing techniques when combined with pattern classification analysis can provide an automated fault detection procedure for machinery diagnostics. Artificial neural networks have recently been established as a powerful method of pattern recognition. The neural networkbased fault detection approach usually requires preprocessing algorithms which enhance the fault features, reducing their number at the same time. Various timeinvariant and timevariant signal preprocessing algorithms are studied here. These include spectral analysis, time domain averaging, envelope detection, Wigner-Ville distributions and wavelet transforms. A neural network pattern classifier with preprocessing algorithms is applied to experimental data in the form of vibration records taken from a controlled tooth fault in a pair of meshing spur gears. The results show that faults can be detected and classified without errors. 相似文献
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Qing Zhao Zhihan Xu 《IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics》2004,34(2):1089-1095
In this paper, a real-time fault detection and isolation (FDI) scheme for dynamical systems is developed, by integrating the signal processing technique with neural network design. Wavelet analysis is applied to capture the fault-induced transients of the measured signals in real-time, and the decomposed signals are pre-processed to extract details about a fault. A Regional Self-Organizing feature Map (R-SOM) neural network is synthesized to classify the fault types. The R-SOM neural network adopts two regions adjustment in the learning algorithm, thus it has high precision in clustering and matching, especially when the noise, disturbance and other uncertainties exist in the systems. As a result, the proposed FDI scheme is robust and accurate. The design is implemented on a stirred tank system and satisfactory online testing results are obtained. 相似文献