共查询到17条相似文献,搜索用时 171 毫秒
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对股票预测问题进行了深入的研究,提出了一个新的预测方法.针对股票时间序列的高度非线性、高噪音的特点,采用小波变换方法有效的过滤噪音、约简数据,并对ARIMA模型和BP神经网络预测模型进行了研究和分析,提出了一个基于ARIMA模型和BP神经网络模型的模糊变权重组合预测模型,应用该模型对股票时间序列进行分析预测,取得了令人满意的效果. 相似文献
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引入改进的粒子群算法对小波核函数最小二乘支持向量机进行优化,提出了位移时间序列预测的改进粒子群优化小波最小二乘支持向量机预测模型(APSO-WLSSVM)。该模型具有小波变换的良好时、频域分辨能力和支持向量机的非线性学习能力;同时利用粒子群算法优化小波最小二乘支持向量机的参数,避免了人为选择参数的盲目性,从而提高了模型的预测精度。为证明该模型的优越性,将该模型与传统的高斯核函数支持向量机模型的预测结果作了对比,结果表明该模型较传统方法预测精度有了明显提高。最后将该模型用于锦屏一级水电站左岸边坡和导流洞进行变形预测,预测结果表明该方法科学可靠,在岩土体位移时序预测中具有良好的实际应用价值。 相似文献
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冲击地压AE时间序列小波神经网络预测模型 总被引:9,自引:3,他引:6
针对冲击地压监测AE时间序列的特点,建立了由伸缩和平移因子决定的小波基函数代替Sigmoid等传递函数的小波神经网络预测模型,避免了传统神经网络需要人为干预网络结构参数的不足。实例分析表明,该模型拟和预测精度高,具有重要的应用价值。 相似文献
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针对短时交通流时间序列的缺点,应用小波变换理论,将含有综合信息的时间序列分离为低频确定信号和高频干扰信号,用遗传过程神经元网络分别进行预测,得到了原时间序列的实际预测结果,通过实测数据验证表明,该预测方法具有较好的预测精度。 相似文献
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针对于传统的确定性太阳辐射模型不能反映气象变化的弊端,提出了基于回归BP神经网络和小波分析理论的太阳散射辐射逐日预测模型。神经网络具有非线性函数逼近及自组织自学习的能力,基于小波分析在信号处理方面的时频域多分辨特性,本文利用小波变换将太阳散射辐射数据序列进行时频域分解后作为神经网络预测模型的输入样本,实例表明该方法与传统模型相比预测精度高,具有可行性。 相似文献
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准确预测空调负荷不仅对蓄能空调高效运行意义重大,而且也是冷热电三联产技术发挥优势的关键所在。本文提出一种小波网络应用于空调负荷的预测模型,通过小波分解,把空调负荷序列分解为不同频段的小波系数序列,再将各层的小波系数子序列重构到原尺度上,然后对小波系数序列采用相匹配的BP神经网络模型进行预测,最后合成空调负荷序列的最终预测结果。该预测模型中的低频小波系数a3和中频小波系数d3的神经网络输入变量为前1天小波系数值和对应时刻的温度、相对湿度、风速、总辐射量、天气状况和星期几编码共7个因子,并采用主成分分析法进行输入变量的降维;高频小波系数d2和d1以前几日的小波系数为输入因子。经过对西安市某综合楼的空调负荷进行预测,证明了预测值和实际运行值拟和很好,相对误差为-10%~8%。该预测模型具有预测精度较高、推广能力较强及计算速度较快的优点。 相似文献
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指出小波多分辨分析具有较强的时频分析特性,对含有趋势性、周期性和随机性的非线性变形时间序列进行分解,用不同的模型对各分解项进行预测后叠加,比单纯用某一种模型对变形的预测,精度有较大的提高,对各分解项用时间序列分析模型预测周期性,用多项武拟合趋势性,结果显示,预测效果较好。 相似文献
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Wavelet and ANN combination model for prediction of daily suspended sediment load in rivers 总被引:7,自引:0,他引:7
Rajaee T 《The Science of the total environment》2011,409(15):2917-2928
In this research, a new wavelet artificial neural network (WANN) model was proposed for daily suspended sediment load (SSL) prediction in rivers. In the developed model, wavelet analysis was linked to an artificial neural network (ANN). For this purpose, daily observed time series of river discharge (Q) and SSL in Yadkin River at Yadkin College, NC station in the USA were decomposed to some sub-time series at different levels by wavelet analysis. Then, these sub-time series were imposed to the ANN technique for SSL time series modeling. To evaluate the model accuracy, the proposed model was compared with ANN, multi linear regression (MLR), and conventional sediment rating curve (SRC) models. The comparison of prediction accuracy of the models illustrated that the WANN was the most accurate model in SSL prediction. Results presented that the WANN model could satisfactorily simulate hysteresis phenomenon, acceptably estimate cumulative SSL, and reasonably predict high SSL values. 相似文献
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W. C. Su C. S. Huang C. H. Chen C. Y. Liu H. C. Huang Q. T. Le 《Computer-Aided Civil and Infrastructure Engineering》2014,29(10):738-757
Ambient vibration tests are conducted widely to estimate the modal parameters of a structure. The work proposes an efficient wavelet‐based approach to determine the modal parameters of a structure from its ambient vibration responses. The proposed approach integrates the time series autoregressive (AR) model with the stationary wavelet packet transform. In addition to providing a richer decomposition and allowing for an improved time–frequency localization of signals over that of the discrete wavelet transform, the stationary wavelet packet transform also has significantly higher computational efficiency than the wavelet packet transform in terms of decomposing time‐shifted signals because the former has a time‐invariance property. The correlation matrices needed in determining the coefficient matrices in an AR model are established in subspaces expanded by stationary wavelet packets. The formulation for estimating the correlation matrices is shown for the first time. Because different subspaces contain signals with different frequency subbands, the fine filtering property enhances the ability of the proposed approach to identify not only the modes with strong modal interference, but also many modes from the responses of very few measured degrees of freedom. The proposed approach is validated by processing the numerically simulated responses of a seven‐floor shear building, which has closely spaced modes, with considering the effects of noise and incomplete measurements. Furthermore, the present approach is employed to process the velocity responses of an eight‐storey steel frame subjected to white noise input in a shaking table test and ambient vibration responses of a cable‐stayed bridge. 相似文献
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在数据分析处理中,小波分析与灰色模型有各自的特点和适用范围。结合两者的优点,构建小波灰色串联和并联组合模型,并应用于变形监测工程实例,验证了组合模型建模和预测的可行性和有效性。分析结果表明,在变形监测数据处理中,应用小波灰色组合模型有较高的预测精度,能有效地进行变形监测数据处理和预测预报。 相似文献
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Abstract: Accurate and timely forecasting of traffic flow is of paramount importance for effective management of traffic congestion in intelligent transportation systems. A detailed understanding of the properties of traffic flow is essential for building a reliable forecasting model. The discrete wavelet packet transform (DWPT) provides more coefficients than the conventional discrete wavelet transform (DWT), representing additional subtle details of a signal. In wavelet multiresolution analysis, an important decision is the selection of the decomposition level. In this research, the statistical autocorrelation function (ACF) is proposed for the selection of the decomposition level in wavelet multiresolution analysis of traffic flow time series. A hybrid wavelet packet-ACF method is proposed for analysis of traffic flow time series and determining its self-similar, singular, and fractal properties. A DWPT-based approach combined with a wavelet coefficients penalization scheme and soft thresholding is presented for denoising the traffic flow. The proposed methodology provides a powerful tool in removing the noise and identifying singularities in the traffic flow. The methods created in this research are of value in developing accurate traffic-forecasting models . 相似文献