共查询到19条相似文献,搜索用时 125 毫秒
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介绍了用神经网络校正传感器系统非线性误差的原理和方法,提出了一种基于RBF神经网络的传感器非线性校正模型及其算法,并与采用BP神经网络校正非线性误差进行了比较,并给出一个仿真实验,实验结果表明:采用RBF神经网络的传感器非线性校正精度和网络训练速度均大大优于BP神经网络,能满足实用要求. 相似文献
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针对磁罗盘传感器非线性校正中现有方法的不足,提出采用小波函数和双曲正弦函数作为超限学习机(ELM)的激活函数,并将此改进超限学习机用于磁罗盘的校正.同时,阐述了传感器的非线性校正原理,磁罗盘航向误差模型及改进超限学习机的实现过程,并分别采用BP神经网络法和传统ELM对磁罗盘进行非线性校正.实验结果表明,改进ELM算法补偿后最大误差为0.103°,均方根误差为0.0596°,优于BP神经网络算法(补偿后最大误差为0.5°,均方根误差为0.1805°)和传统ELM神经网络(补偿后最大误差为0.21°,均方根误差为0.1056°). 相似文献
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本文结合笔者所设计的超声波测距仪,介绍了用神经网络校正超声波传感器的非线性误差的原理与方法,并提出了基于BP神经网络的超声波测距非线性误差校正的模型、算法及其硬件实现。通过理论分析和硬件实验,显示出BP神经网络对超声波传感器的温度补偿和非线性校正的效果良好,充分表明了应用神经网络在提高超声波测距精度方面是一种行之有效的方法。 相似文献
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基于BP神经网络的浓度传感器非线性校正 总被引:1,自引:0,他引:1
提出基于BP神经网络的浓度传感器非线性误差校正方法。文中详细给出了BP神经网络算法原理及训练方案。当替换传感器或环境条件发生变化时,只要获取一组输入输出样本对,便可重新训练网络,获得新的输入输出样本关系,从而实现传感器非线性校正和动态标定,提高传感器的互换性,有实际应用价值。 相似文献
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基于PSO的神经网络在传感器
数据融合中的应用 总被引:1,自引:0,他引:1
针对压力传感器对温度存在交叉灵敏度这一具体问题,常采用BP神经网络对其进行数据融合.但BP神经网络方法训练收敛速度慢,易陷入局部最优.采用PSO全局优化算法训练多层前向神经网络权值,使网络训练误差比BP方法降低了两个数量级,并且收敛速度明显加快.融合结果表明基于PSO神经网络方法更有效地消除了温度对压力传感器的影响,显著提高了传感器的稳定性和准确度. 相似文献
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针对BP网络存在着收敛速度慢和局部极小的问题,提出了一种基于径向基神经网络的汇率预测研究方法。将经济变量数据归一化处理,然后送入径向基神经网络(RBF)中训练,得出相应参数,再对汇率进行预测。详细的仿真实验以及与BP神经网络的比较表明,该方法不仅运算速度较快,且预测精度明显要高于传统BP神经网络所能达到的效果。 相似文献
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Stock market is considered chaotic, complex, volatile and dynamic. Undoubtedly, its prediction is one of the most challenging tasks in time series forecasting. Moreover existing Artificial Neural Network (ANN) approaches fail to provide encouraging results. Meanwhile advances in machine learning have presented favourable results for speech recognition, image classification and language processing. Methods applied in digital signal processing can be applied to stock data as both are time series. Similarly, learning outcome of this paper can be applied to speech time series data. Deep learning for stock prediction has been introduced in this paper and its performance is evaluated on Google stock price multimedia data (chart) from NASDAQ. The objective of this paper is to demonstrate that deep learning can improve stock market forecasting accuracy. For this, (2D)2PCA + Deep Neural Network (DNN) method is compared with state of the art method 2-Directional 2-Dimensional Principal Component Analysis (2D)2PCA + Radial Basis Function Neural Network (RBFNN). It is found that the proposed method is performing better than the existing method RBFNN with an improved accuracy of 4.8% for Hit Rate with a window size of 20. Also the results of the proposed model are compared with the Recurrent Neural Network (RNN) and it is found that the accuracy for Hit Rate is improved by 15.6%. The correlation coefficient between the actual and predicted return for DNN is 17.1% more than RBFNN and it is 43.4% better than RNN. 相似文献
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岩性识别是测井数据解释中最关键的一环,但传统的岩性识别方法解释效率慢,精度低,受人为因素影响大。为此,提出一种遗传优化径向基概率神经网络(RBPNN)的岩性识别方法。该方法融合概率神经网络(PNN)和径向基函数神经网络(RBFNN)的优势来构造RBPNN,采用遗传算法搜索使得RBPNN训练法误差最小的最优隐中心矢量和相匹配的核函数控制参数,优化网络结构,提高收敛速度与精度,形成全结构遗传优化的RBPNN模型。实例应用表明,基于遗传优化RBPNN的岩性识别能够达到工程实际应用的规范标准,且是可行有效的,能够为油田地质勘探领域的岩性识别提供科学的理论支持与依靠。 相似文献
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门诊量预测是现代医院电梯交通以及医疗资源优化配置的重要前提。为了有效地预测医院的门诊量,提出一种将灰色预测方法与RBF神经网络有机结合的灰色神经网络组合预测方法。该方法利用灰色预测中的累加生成运算(AGO)对原始观测数据进行变换,得到规律性较强的累加数据,作为神经网络的建模和训练样本。所提出的方法既避免了灰色预测方法存在的理论误差,又提高了神经网络的训练速度和预测精度,对短期的医院门诊量预测具有较强的实用价值。结果表明:所提出的方法具有良好的预测精度。 相似文献
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Manoj Tripathy 《Simulation Modelling Practice and Theory》2010,18(5):600-611
Many methods have been used to discriminate magnetizing inrush from internal faults in power transformers. Most of them follow a deterministic approach, i.e. they rely on an index and fixed threshold. This article proposes two approaches (i.e. NNPCA and RBFNN) for power transformer differential protection and address the challenging task of detecting magnetizing inrush from internal fault. These approaches based on the pattern recognition technique. In the proposed algorithm, the Neural Network Principal Component Analysis (NNPCA) and Radial Basis Function Neural Network (RBFNN) are used as a classifier. The principal component analysis is used to preprocess the data from power system in order to eliminate redundant information and enhance hidden pattern of differential current to discriminate between internal faults from inrush and over-excitation condition. The presented algorithm also makes use of ratio of voltage-to-frequency and amplitude of differential current for detection transformer operating condition. For both proposed cases, optimal number of neurons has been considered in the neural network architectures and the effect of hidden layer neurons on the classification accuracy is analyzed. A comparison among the performance of the FFBPNN (Feed Forward Back Propagation Neural Network), NNPCA, RBFNN based classifiers and with the conventional harmonic restraint method based on Discrete Fourier Transform (DFT) method is presented in distinguishing between magnetizing inrush and internal fault condition of power transformer. The algorithm is evaluated using simulation performed with PSCAD/EMTDC and MATLAB. The results confirm that the RBFNN is faster, stable and more reliable recognition of transformer inrush and internal fault condition. 相似文献
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针对径向基函数(Radial Basis Functions,RBF)神经网络结构参数确定问题,提出了一种基于蛙跳算法优化RBF神经网络参数的新方法。将RBF神经网络参数组成一个多维向量,作为蛙跳算法中的参数进行优化。以适应度函数为标准,在可行解空间中搜索最优解,并对蛙跳算法进行了改进。非线性函数逼近实验结果表明,该优化算法相对标准遗传优化算法、粒子群优化算法有较小的均方误差,具有更好的逼近能力。 相似文献