共查询到20条相似文献,搜索用时 218 毫秒
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赵宝江 《计算机工程与应用》2011,47(21):153-156
基于T-S模型,提出一种非线性系统的模型辨识方法。利用蚁群聚类算法来进行结构辨识,确定系统的模糊空间和模糊规则数。在聚类的基础上,利用遗传算法辨识模糊模型的后件加权参数,得到一个精确的模糊模型,从而实现参数辨识。仿真结果验证了该方法的有效性,表明该方法能够实现非线性系统的辨识,辨识精度高,可当作复杂系统建模的一种有效手段。 相似文献
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对TEAMS和eXpress两款主流测试性建模与分析软件所采用的测试性框图模型——多信号模型和混合诊断模型,从产品结构单元表示、端口表示、功能(信号)表示、故障模式表示、测试表示等多个方面进行了深入比较研究,并对二者进行了综合评价;在此基础上,综合两者特性提出了一种新的测试性框图模型改进方案——端信号模型;通过将组元端口所通过信息流直接视作信号、引入结点、扩充信号映射定义、允许定义多值测试等多种设置,端信号模型方案能够支持基于电路网表创建模型和更为精确化的故障-测试关联性描述。 相似文献
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信号采集是实现船舶智能化的技术基础,针对船舶微弱信号传感器,提出一种基于single board RIO(简称sbRIO)的多通道多类微弱小信号兼容采集与处理方法.首先提出了多通道多类微弱小信号采集与处理总体方案,分析了传感器输出信号特点和采集性能指标要求,然后从软件和硬件两个方面详细阐述了采集处理机制原理和具体实现方案;最后搭建了原型板卡和上位机软件,进行了36通道测试、本底噪声测试、信号一致性测试、传感器兼容接入测试和多通道信号并行采集测试等功能性能指标的详细测试.实验结果表明,提出的采集方案可兼容接入多类传感器,同时本底噪声小、通道一致性良好,具有出色的采集调理性能,满足指标要求. 相似文献
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由于传统音乐信号辨识方法存在精度低、效率差等缺陷,无法适应现代电子音乐发展速度,因此提出基于深度学习的电子音乐信号辨识方法。首先,采集电子音乐信号,通过预加重、加窗分帧操作预处理信号,剔除夹杂的干扰信号;其次,建立一个深度学习模型,利用模型训练、测试实现电子音乐信号辨识;最后,进行实验对比分析。实验结果表明,本文设计方法对多类型的电子音乐信号辨识正确率为96.42%,辨识时间为1.82s,优于其他方法。 相似文献
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针对实际工业过程中普遍存在有色噪声,提出了有色噪声干扰下Hammerstein非线性系统两阶段辨识方法。采用设计的组合式信号实现Hammerstein系统各模块参数辨识分离,简化了辨识过程。在第一阶段,基于可分离信号的输入输出数据,利用相关分析算法估计线性模块参数,减少了有色噪声对辨识的干扰。在第二阶段,基于随机信号的输入输出数据,在最小二乘算法中引入滤波技术,推导了滤波递推增广最小二乘算法,提高了非线性模块参数和噪声模型参数的辨识精度。仿真结果表明:提出的两阶段辨识方法提高了辨识精度,有效地抑制了有色噪声的干扰。 相似文献
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含有迟滞的三明治系统不仅具有非光滑、多值映射等特性, 而且迟滞环节的输入输出信号还是不能直接测量的, 常规方法难以进行有效的辨识. 本文提出了一种基于退化激励信号的两步辨识方案: 第一步, 设计一个特殊的退化激励信号将迟滞环节退化为一条静态曲线, 从而可以将两端的线性动态环节辨识出来, 解决中间信号不可测的问题; 第二步, 利用已辨识的线性模型重构迟滞环节的输入输出信号, 再采用“扩展输入空间法”建立迟滞环节的神经网络模型. 最后, 在压电超精密运动系统的实验结果表明所提出的建模方法取得了令人满意的结果. 相似文献
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针对控制系统的传递函数建模与控制器的参数优化问题,提出了基于Prony和微粒群优化(PSO)算法的设计方案。首先在被控对象的输入端施加一个脉冲信号,然后对其输出信号进行Prony分析,得出该被控对象的传递函数,最后采用改进PSO算法进行控制器的参数优化设计。基于辨识的Prony算法可快速准确得出被控对象的传递函数;基于T-S模型模糊自适应的改进PSO算法(T-SPSO算法)依据种群当前最优性能指标和惯性权重自适应惯性权重取值,较好解决了PSO算法的早熟问题,可以更好地优化控制器参数。该方案实现了控制系统的精确建模与优化设计,仿真结果验证了所提方案的有效性。 相似文献
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This paper proposes using fractal QRS-complexes pattern recognition for imperative cardiac arrhythmias. A typical electrocardiogram (ECG) signal is comprised of P-wave, QRS-complex, and T-wave. Fractal dimension transformation (FDT) is employed to adjoin the QRS-complex from time-domain ECG signals, including the fractal features of supraventricular ectopic beat, bundle branch ectopic beat, and ventricular ectopic beat. FDT with fractal dimension (FD) is addressed for constructing various symptomatic features, and can produce family functions and enhance features, making the difference between healthy and unhealthy subjects more significant. The probabilistic neural network (PNN) is proposed for recognizing the states of cardiac physiologic function. The proposed method is tested using the MIT–BIH (Massachusetts Institute of Technology–Beth Israel Hospital) arrhythmia database. Compared with other methods, the numerical experiments demonstrate greater efficiency and higher accuracy in recognizing ECG signals. 相似文献
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Osamu Fukuda Jonghwan Kim Isao Nakai Yasunori Ichikawa 《Artificial Life and Robotics》2011,16(1):90-93
This article presents the control method for a 5-fingered artificial hand using electromyography (EMG) signals. Our targeted
artificial hand is driven by pneumatic actuators to reduce its weight, and we use ON/OFF solenoid valves instead of electro
pneumatic regulators to simplify the control system. The pneumatic hand has 15 degrees of freedom, and it seems difficult
to reproduce all the finger motions from the EMG signals only. Therefore, we describe typical hand motions using a Petri net,
and control the finger motions efficiently based on this model. Each state of the Petri net indicates one step in the hand
posture to complete the intended motion. Simultaneously, this state corresponds to the ON/OFF pattern of the 15 solenoid valves.
This enables the operator to control the 5-fingered dexterous hand smoothly, transiting the state in the Petri net according
to the EMG motion signals. We conducted an experiment to verify the validity of the proposed method. In the experiment, five
typical motions (spherical grasp, power grip, hook grip, key grip, precision grip) were successfully performed using the 6-channel
EMG signals measured from the operator’s forearm. 相似文献
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This article explores the ability of multivariate autoregressive model (MAR) and scalar AR model to extract the features from two-lead electrocardiogram signals in order to classify certain cardiac arrhythmias. The classification performance of four different ECG feature sets based on the model coefficients are shown. The data in the analysis including normal sinus rhythm, atria premature contraction, premature ventricular contraction, ventricular tachycardia, ventricular fibrillation and superventricular tachycardia is obtained from the MIT-BIH database. The classification is performed using a quadratic discriminant function. The results show the MAR coefficients produce the best results among the four ECG representations and the MAR modeling is a useful classification and diagnosis tool. 相似文献
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《Expert systems with applications》2014,41(16):7161-7170
The features extracted from the cardiac sound signals are commonly used for detection and identification of heart valve disorders. In this paper, we present a new method for classification of cardiac sound signals using constrained tunable-Q wavelet transform (TQWT). The proposed method begins with a constrained TQWT based segmentation of cardiac sound signals into heart beat cycles. The features obtained from heart beat cycles of separately reconstructed heart sounds and murmur can better represent the various types of cardiac sound signals than that from containing both. Therefore, heart sounds and murmur have been separated using constrained TQWT. Then the proposed novel raw feature set has been created by the parameters that have been optimized while constraining the output of TQWT together with that of extracted by using time-domain representation and Fourier–Bessel (FB) expansion of separated heart sounds and murmur. However, the adaptively selected features have been used to obtain the final feature set for subsequent classification of cardiac sound signals using least squares support vector machine (LS-SVM) with various kernel functions. The performance of the proposed method has been validated with publicly available datasets and the results have been compared with the existing short-time Fourier transform (STFT) based method. The proposed method shows higher percentage classification accuracy of 94.01 as compared to 93.53 of STFT based method. In comparison with STFT based method, it is noteworthy that the proposed method uses well defined and lower dimensionality of feature vector that can reduce the computational complexity. 相似文献
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在统计自相关函数的基础上,提出一种对雷达辐射源信号进行脉内特征提取的方法。利用一阶差分运算突出信号的调制特性;将差分的结果进行自相关计算,提取不同时延下自相关函数的包络特征;根据提出的基于距离的可分性判据对包络进行特征选择,得到具有最优可分性能的二维或三维特征向量。通过对7种典型辐射源信号的特征提取和分类进行仿真实验,结果表明提取的特征在低信噪比下仍具有较好的抗噪性和可分类性。 相似文献
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Random neural networks with multiple classes of signals 总被引:3,自引:0,他引:3
By extending the pulsed recurrent random neural network (RNN) discussed in Gelenbe (1989, 1990, 1991), we propose a recurrent random neural network model in which each neuron processes several distinctly characterized streams of "signals" or data. The idea that neurons may be able to distinguish between the pulses they receive and use them in a distinct manner is biologically plausible. In engineering applications, the need to process different streams of information simultaneously is commonplace (e.g., in image processing, sensor fusion, or parallel processing systems). In the model we propose, each distinct stream is a class of signals in the form of spikes. Signals may arrive to a neuron from either the outside world (exogenous signals) or other neurons (endogenous signals). As a function of the signals it has received, a neuron can fire and then send signals of some class to another neuron or to the outside world. We show that the multiple signal class random model with exponential interfiring times, Poisson external signal arrivals, and Markovian signal movements between neurons has product form; this implies that the distribution of its state (i.e., the probability that each neuron of the network is excited) can be computed simply from the solution of a system of 2Cn simultaneous nonlinear equations where C is the number of signal classes and n is the number of neurons. Here we derive the stationary solution for the multiple class model and establish necessary and sufficient conditions for the existence of the stationary solution. The recurrent random neural network model with multiple classes has already been successfully applied to image texture generation (Atalay & Gelenbe, 1992), where multiple signal classes are used to model different colors in the image. 相似文献
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The automatic and accurate arrhythmia diagnosis in the electrocardiogram (ECG) signals is significant for cardiac health. Typically, the arrhythmia diagnosis is automatically detected depending on single-lead signals or a simple combination of multilead signals from the ECG. However, it ignores the inter-lead correlation and the significance of different leads for different heart beats detection, which decreases the performance of arrhythmia diagnosis. In this paper, arrhythmia diagnosis is converted to a problem of multigranulation computing in the view of granular computing, and thus different lead signals can be captured to improve the effectiveness of abnormal heart beats detection. To this end, multilead ECG signals are firstly granulated into different fuzzy information granules by the fuzzy equivalence relation. An objective decision-making model based on fuzzy set theory is then proposed for describing and analyzing these granulated multilead ECG signals, which brings a self-adaptive and unsupervised decision making. As a result, the significance and correlation of different leads are analyzed by granularity selection and granular structures to make a better decision for arrhythmia diagnosis. Extensive experimental results show that the proposed algorithm can significantly improve the performance of arrhythmia diagnosis, especially better robustness to several types of cardiac arrhythmia. 相似文献