共查询到20条相似文献,搜索用时 15 毫秒
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基于神经网络集成的软件故障预测及实验分析 总被引:1,自引:0,他引:1
软件系统故障预测是软件测试过程中软件可靠性研究的重点之一。利用软件系统测试过程中前期的故障相关信息进行建模,预测后期的软件故障信息,以便于后期测试和验证资源的合理分配。根据软件测试过程中已知的软件故障时间序列,利用非齐次泊松分布过程、神经网络、神经网络集成等方法对其进行建模。通过对三个实例分别建模,其预测平均相对误差G-O模型依次为3.02%、5.88%和6.58%,而神经网络集成模型为0.19%、1.88%和1.455%,实验结果表明神经网络集成模型具有更精确的预测能力。 相似文献
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针对单一长短时记忆(LSTM)网络在航迹预测上无法有效提取关键信息以及难以精准拟合数据分布等问题,提出基于注意力机制和生成对抗网络(GAN)的飞行器短期轨迹预测模型。首先,引入注意力机制对航迹赋予不同的权重,以提升航迹中重要特征的影响力;其次,基于LSTM提取航迹序列特征,并经汇聚层汇集时间步长内所有的飞行器特征;最后,利用GAN在对抗博弈下不断优化的特性来优化模型,从而提高模型的准确性。相较于社会生成对抗网络(SGAN),所提模型在处于爬升阶段的数据集上的平均位移误差(ADE)、最终位移误差(FDE)及最大位移误差(MDE)分别降低了20.0%、20.4%和18.3%。实验结果表明,所提模型能更精确地预测未来航迹。 相似文献
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Tool wear prediction is of significance to improve the safety and reliability of machining tools, given their widespread applications in nearly every branch of manufacturing. Mathematical modelling, including data driven modelling and physics-based modelling, is an important tool to predict the degree of tool wear. Howerver, the performance of conventional data driven models is restricted by the absent representation of physical inconsistency. The physics-based models usually fail to consider the complex tool cutting conditions and dynamic changes of physical parameters in practice. To address these issues, a novel physics guided neural network model is presented for tool wear prediction. Firstly, a cross physics-data fusion (CPDF) scheme is proposed as the modelling strategy to fuse the hidden information explored by a physics-based model and a data driven model. Secondly, the information hidden in the unlabelled sample is explored by the physics-based model of tool cutting, inspired by semi-supervised learning. Thirdly, a novel loss function which takes the physical discipline into account is proposed to evaluate the physical inconsistency quantitatively. The advantage of the developed method is that it explores sufficient information from both physics and data domains to eliminate the physical inconsistency existing in conventional data driven models. 相似文献
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王华 《计算机工程与应用》2011,47(26):242-245
煤自然发火期是衡量煤自燃特性的一个重要参数,也是指导井下防灭火工作的重要参考依据。结合主成分分析与神经网络的优点,提出了基于主成分分析的神经网络煤自然发火期预测模型。采用主成分分析法对原始输入变量进行预处理,选择输入变量的主成分作为神经网络输入,一方面减少了输入变量的维数,消除了各输入变量的相关性;另一方面提高了网络的收敛性和稳定性,同时也简化了网络的结构。通过实例验证,基于主成分的神经网络比一般神经网络训练精度更高,学习时间更短,预测效果更优。 相似文献
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小波与神经网络相结合的网络流量预测模型 总被引:1,自引:0,他引:1
针对网络流量序列的非线性和多时间尺度特性,提出了一种将小波变换与人工神经网络相结合进行网络流量预测的新模型.该模型吸取了小波变换的多分辨功能和人工神经网络的非线性逼近能力,对流量时间序列进行小波分解,得到小波变换尺度系数序列和小波系数序列,分别使用RBF神经网络和Elman神经网络进行预测,把两种预测的结果通过BP神经网络合成为最终预测结果.用实际网络流量对该模型进行验证,结果表明,该模型具有较高的预测效果. 相似文献
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P. Ramasubramanian A. Kannan 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2006,10(8):699-714
Information systems are one of the most rapidly changing and vulnerable systems, where security is a major issue. The number
of security-breaking attempts originated inside the organizations are increasing steadily. Attacks made in this way, usually
done by ``authorized' users of the system, cannot be immediately traced. As the idea of filtering the traffic at the entrance
door, by using firewalls and the like, is not completely successful, the use of intrusion detection systems should be considered
to increase the defense capacity of an information system. This paper presents a framework for a statistical anomaly prediction
system using a neuro-genetic forecasting model, which predicts unauthorized invasions of user, based on previous observations
and takes further action before intrusion occurs. In this paper, we propose an evolutionary time-series model for short-term
database intrusion forecasting using genetic algorithm owing to its global search capability. The experimental results show
that the combination strategy(neuro-genetic) can quicken the learning speed of the network and improve the predicting precision
compared to the traditional artificial neural network. This paper also focuses on detecting significant changes of transaction
intensity for intrusion prediction. The experimental study is performed using real time data provided by a major Corporate
Bank. Furthermore, a comparative evaluation of the proposed neuro-genetic model with the traditional feed-forward network
trained by the back-propagation with momentum and adaptive learning rate using sum square error on a prediction data set has
been presented and a better prediction accuracy has been observed. 相似文献
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为衡量网络运行负荷和运行状态,对网络进行合理规划,在对目前网络流量预测模型进行了研究的基础上,结合灰色模型和神经网络模型在反映数据的趋势性变化上的明显效果,以及神经网络补偿器,提出了基于补偿器的灰色神经网络流量预测模型,仿真结果验证了所提方法的有效性。 相似文献
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网络流量是衡量网络运行负荷和状态的重要参数,也是网络规划、流量管理等方面起着重要作用的重要参数。在流量管理中,流量模型用于评价接入控制机制和预测网络性能。灰色模型和神经网络在反映数据的趋势性变化上效果明显,随着灰色神经网络的发展及其广泛应用,越来越多的方法已经被提出。文中利用神经网络补偿器获得误差补偿信号,则最终的预测值为灰色神经网络模型的预测值加上误差补偿。仿真结果验证了所提方法的有效性。 相似文献
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王丽 《计算机工程与应用》2010,46(22):80-82
首先利用一种改进后的粒子群算法对BP神经网络权值的选取进行优化,然后以LAN/WLAN集成网络为背景,用三种方法(BP神经网络、改进PSO算法优化后的BP神经网络、SVM)建立了LAN/WLAN集成网络可靠性的预测模型,最后通过实验比较,证明了改进后的神经网络模型预测通信网的可靠性、有效性和优越性。 相似文献
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A neural network model that processes financial input data is developed to estimate the market price of options at closing. The network's ability to estimate closing prices is compared to the Black-Scholes model, the most widely used model for the pricing of options. Comparisons reveal that the mean squared error for the neural network is less than that of the Black-Scholes model in about half of the cases examined. The differences and similarities in the two modeling approaches are discussed. The neural network, which uses the same financial data as the Black-Scholes model, requires no distribution assumptions and learns the relationships between the financial input data and the option price from the historical data. The option-valuation equilibrium model of Black-Scholes determines option prices under the assumptions that prices follow a continuous time path and that the instantaneous volatility is nonstochastic. 相似文献
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股票价格受多种因素的综合影响,具有趋势性、较大波动性和随机性等变化特点,单一模型难准确对其变化规律进行准确描述,将灰色理论和BP神经网络相结合构建一种股票价格组合预测模型。采用灰色GM(1,1)预测模型动态预测股票价格变化趋势,运用BP神经网络对灰色GM(1,1)模型预测结果进行修正,以提高股票价格预测精度。采用ST东北高(600003)股票价格对预测模型性能进行测试,结果表明,组合预测模型提高了股票价格的预测精度,更能挖掘股票价格变化规律。 相似文献
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Shi-Ming Huang Chih-Fong Tsai David C. Yen Yin-Lin Cheng 《Expert systems with applications》2008,35(3):1034-1040
Accounting frauds have continuously happened all over the world. This leads to the need of predicting business failures. Statistical methods and machine learning techniques have been widely used to deal with this issue. In general, financial ratios are one of the main inputs to develop the prediction models. This paper presents a hybrid financial analysis model including static and trend analysis models to construct and train a back-propagation neural network (BPN) model. Further, the experiments employ four datasets of Taiwan enterprises which support that the proposed model not only provides a high predication rate but also outperforms other models including discriminant analysis, decision trees, and the back-propagation neural network alone. 相似文献
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This paper proposes a novel model by evolving partially connected neural networks (EPCNNs) to predict the stock price trend using technical indicators as inputs. The proposed architecture has provided some new features different from the features of artificial neural networks: (1) connection between neurons is random; (2) there can be more than one hidden layer; (3) evolutionary algorithm is employed to improve the learning algorithm and training weights. In order to improve the expressive ability of neural networks, EPCNN utilizes random connection between neurons and more hidden layers to learn the knowledge stored within the historic time series data. The genetically evolved weights mitigate the well-known limitations of gradient descent algorithm. In addition, the activation function is defined using sin(x) function instead of sigmoid function. Three experiments were conducted which are explained as follows. In the first experiment, we compared the predicted value of the trained EPCNN model with the actual value to evaluate the prediction accuracy of the model. Second experiment studied the over fitting problem which occurred in neural network training by taking different number of neurons and layers. The third experiment compared the performance of the proposed EPCNN model with other models like BPN, TSK fuzzy system, multiple regression analysis and showed that EPCNN can provide a very accurate prediction of the stock price index for most of the data. Therefore, it is a very promising tool in forecasting of the financial time series data. 相似文献