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1.
针对复杂装备故障信息不足、故障预测困难等问题,应用支持向量机建立了故障预测模型;在对支持向量机回归算法分析的基础上,利用最小二乘支持向量机建立故障预测模型;最小二乘支持向量机通过对相空间重构,有效地降低了模型的复杂度;最后,本文利用某导弹发射装置液压泵的故障数据进行了验证,通过选取合适的参数,该模型能够较好地对故障数据进行预测,预测精度较高;事实证明,基于最小二乘支持向量机建立故障预测模型能够较好地对复杂装备故障的趋势进行预测。  相似文献   

2.
采用傅里叶变换红外光声光谱技术对10个品种的油菜籽样本进行品种鉴别。原始光声光谱卷积平滑后,首先采用全谱数据建立支持向量机鉴别模型,当RBF核函数的核参数γ值为0.01时,模型最大预测率为70%。利用方差分析的方法对全谱进行有效波长筛选,筛选后的波长用于建立支持向量机鉴别模型,当γ值取0.1时,模型的识别率和预测率均可达到100%。同时,采用偏最小二乘判别分析建立鉴别模型,作为支持向量机模型的对照,该模型的预测率仅为60%,明显低于支持向量机模型的预测精度。研究表明,红外光声光谱技术结合支持向量机,在油菜籽品种鉴别中有良好的应用性能。  相似文献   

3.
为了提高网络舆情预测精度,建立一种基于灰色支持向量机的网络舆情预测模型。首先对网络舆情数据预处理,然后建立网络舆情的GM(1,1)模型,并采用支持向量机对GM(1,1)模型的预测结果修正,最后通过仿真实验对模型性能进行测试。仿真结果表明,相对于传统预测模型,灰色支持向量机提高了网络舆情的预测精度。  相似文献   

4.
鉴于灰色预测方法和支持向量机各自的优点,将灰色预测方法与支持向量机相结合,建立灰色支持向量机模型,并以极差替代收益的标准差度量波动率,运用新模型对深圳基金波动率进行实例分析。通过与v支持向量机的预测结果对比,发现所提出的模型适合于基金波动率的中短期预测。  相似文献   

5.
基于灰色支持向量机的城市用水量预测研究   总被引:1,自引:0,他引:1  
研究城市用水量预测问题,城市需水量具有非线性和随机波动性,城市供水系统是复杂系统,传统灰色模型或支持向量机均无法进行准确预测。为更准确预测城市用水量,建立灰色支持向量机的城市用水量预测模型,以提高预测精度。首先采用灰色模型和支持向量机对城市用水量预测,然后采用多元线性回归确定模型权重系数,最后得到了灰色支持向量机的城市用水量预测结果。仿真结果表明,灰色支持向量机提高了城市用水量的预测精度,预测误差小于单一灰色模型和支持向量机。灰色支持向量机更好地反映出城市用水量变化的总体趋势,可为城市供水系统预测提供参考。  相似文献   

6.
随着视频车牌采集系统的发展与完善,快速路行程时间的动态预测成为了可能。本文根据快速路车牌识别数据的特征和所能提取的信息,结合BP神经网络和支持向量机的预测优点,通过蜂群优化算法对BP神经网络和支持向量机模型的参数进行优化,提出了一种基于多模型融合预测算法(Multi-Model Fusion Algorithm,MMFA)的BP神经网络和支持向量机相结合的组合预测方法。最后选取成都市三环路某段上的视频车牌数据进行实例验证,结果表明该组合预测方法比单一的BP神经网络或者支持向量机具有更好的预测效果。  相似文献   

7.
基于最小二乘支持向量机的耕地面积预测研究*   总被引:1,自引:1,他引:0  
针对耕地面积数据的小样本、复杂非线性特点,提出一种基于最小二乘支持向量机的耕地面积预测方法。采用相关系数法选择耕地面积的影响因子,通过粒子群优化算法对最小二乘支持向量机参数进行优化,最后建立耕地面积与影响因子之间复杂的非线性关系模型。采用湖南省耕地面积数据对模型性能进行验证,结果表明,相对于参比模型,最小二乘支持向量机提高了耕地面积的预测精度,是一种有效的耕地面积预测方法。  相似文献   

8.
对空调负荷进行准确预测不仅对空调优化控制的意义重大,而且也是实现空调经济运行与节能的关键所在。为了提高建筑空调负荷的预测精度,在分析灰色模型和支持向量机建模特点基础上提出了一种空调负荷组合预测算法。该方法综合了灰色建模计算过程简单以及支持向量机自学习和泛化能力强的优点,能够更加有效地利用样本数据的有效信息,提高模型预测精度。首先,通过灰色建模过程弱化了样本数据的随机因素。然后,对灰色模型输出进行归一化处理及数据重构,以作为支持向量机的输入。最后,通过支持向量机模型的预测得到最终预测结果。将本文所提出的方法应用于福州一栋办公建筑的逐时空调负荷预测中,并与灰色模型及支持向量机模型作比较,证明了组合模型的预测值与实际运行值拟合度最高,平均绝对误差比灰色模型和支持向量机模型分别降低了47.84%和17.39%。该组合预测模型具有较高的预测精度和更好的泛化能力,具有较强的可行性和实用性。  相似文献   

9.
研究青霉素发酵优化问题.青霉素发酵过程是一种复杂的生化反应系统,生物参数难获得,许多代谢途径尚不明确,传统方法无法建立精确数学模型.然而支持向量机不需要了解青霉素发酵系统的内部结构,具有智能性、自学习能力,可以解决复杂青霉素发酵建模问题.因此,以青霉素发酵过程生化机理模型产生的数据为样本,训练支持向量机,并采用粒子群算法对支持向量机参数优化,建立了支持向量机的发酵过程模型.仿真结果表明,支持向量机可以对青霉素发酵过程进行状态变量的估算与预测,为有效提高青霉素发酵预测精度,提供了实际有效的指导作用.  相似文献   

10.
在股市投资测试问题的研究中,股价是一种高度不稳定、复杂且难以预测的时间序列数据,传统预测方法都是基于线性模型,忽略了股价的非线性特征,导致预测精度不高.为解决股价预测过程中的精度不高的难题,提出支持向量机引入到股价预测的建模中.首先采用支持向量机非线性扩展样本对时间序列模型定阶,并利用前向浮动特征筛选法选择特征,建立基于支持向量机的股市预测系统模型,对股价进行仿真实验.仿真结果表明,支持向量机模型比神经网络和CAR模型有较高的预测精度,证明适用于股市预测等非线性问题的预测,且有较高的精确度和应用价值.  相似文献   

11.
入境旅游人次预测对旅游管理部门合理配置旅游资源、创新旅游服务模式有很重要的意义。受气候变化、经济发展趋势、文化差异的影响,旅游人次呈现出明显的季节性与非线性特征,管理部门依据经验推断入境旅游过夜人次的难度越来越大。文章采用ARIMA模型,对入境旅游过夜人次进行月度预测更加科学、准确。选取上海市2004-2016年入境旅游月度过夜人次数据为样本,依据AIC、BIC、HQIC最小准则进行参数估计和模型定阶,拟合出入境旅游月度过夜人次预测的最优模型为ARIMA(6,3,0)。运用该模型,对上海市2017年1-12月的入境旅游月度过夜人次进行预测,并将预测值与2017年真实数据比对,其平均绝对误差为3.22%。可见,应用ARIMA对入境旅游月度过夜人次预测有较高信度。  相似文献   

12.
研究湛江电网故障统计分布规律,发现湛江电网故障具有明显的幂律特性,去除故障极值日的数据呈指数分布.而故障极值日均伴有极端天气,表明湛江电网故障的幂律特性因极端天气引发.  相似文献   

13.
为提高风电功率短期预测的准确性,针对KNN(K-Nearest Neighbor algorithm)算法在风电功率预测中的不足,提出了基于K-means和改进KNN算法的风电功率短期预测方法。利用K-means聚类方法确定风电历史样本的类别,对KNN算法中搜索相似历史样本集的方式进行了改进和优化,构建了预测模型,并采用C/S架构实现了预测系统的设计。该系统具有自修正功能,能够随着预测次数的增加,不断修正预测模型,逐渐降低预测的误差率。以吉林省某风电场历史数据为样本进行了仿真分析,结果显示该算法与其它算法相比平均绝对误差和均方根误差最大下降1.08%和0.48%,运算时间提升了5.45%,在风电功率超短期多步预测中具有推广应用价值。  相似文献   

14.
In recent years, electricity crisis still becomes noticeable in some countries due to a widening gap between demand and supply. Consequently, the future demand plays a significant role in efficient management and utilization of electricity. Pertaining to efficient supply handling to increase the power system reliability, an electricity demand forecasting is one of the most crucial tools. The forecasting technique is used by decision makers all over the world to predict the future demand as key information for a proper policy. In this research, the hybrid model consists of maximal overlap discrete wavelet transform (MODWT), support vector machine (SVM), and differential evolution (DE) optimization emphasizing on simplifying the complex structure in data pre-processing is proposed to forecast the thirty-two annual electricity consumptions and is compared with traditional forecasting models, hybrid model of MODWT and SVM, and combined model of SVM and DE optimization based on mean absolute error (MAE), mean absolute percentage error (MAPE) and symmetric mean absolute percentage error (sMAPE) measures as well as Friedman test and post hoc test. The empirical results indicate that the proposed model outperforms other forecasting models and provides more accurate forecasts than other candidate models at 0.05 significance levels and the nearly highest precision. Consequently, the proposed model is able to reduce the limitations of individual models regarding annual electricity consumptions and can be used as a promising tool in order to forecast annual electricity consumptions as well.  相似文献   

15.
In this paper, we adopt the exponentially weighted moving average (EWMA) method to develop the residual modification EWMA grey forecasting model REGM(1,1) and combines it with fuzzy theory to derive the fuzzy REGM or the FREGM(1,1) model. The proposed model is used to forecast annual petroleum demand in Taiwan. The experimental results show that the mean absolute percentage errors, median absolute percentage error, and symmetric mean absolute percentage error of FREGM(1,1) model are higher by 23.71, 12.26, and 23.06% respectively, compared with those obtained using the traditional GM(1,1) model.  相似文献   

16.
The technique of multivariate discount weighted regression is used for forecasting multivariate time series. In particular, the discount regression model is modified to cater for the popular local level model for predicting vector time series. The proposed methodology is illustrated with London metal exchange data consisting of aluminium spot and future contract closing prices. The estimate of the measurement noise covariance matrix suggests that these data exhibit high cross-correlation, which is discussed in some detail. The performance of the proposed model is evaluated via an error analysis based on the mean of squared forecast errors, the mean of absolute forecast errors and the mean of absolute percentage forecast errors. A sensitivity analysis shows that a low discount factor should be used and practical guidelines are given for general future use.  相似文献   

17.
准确实现公路事故伤亡人数预测,对于把握我国未来交通安全形势、实现运输系统优化具有重要意义。将基于稀疏理论的深度自动编码器(Deep Auto-encoder)引入公路事故伤亡人数预测,利用公路事故伤亡人数2000-2015年历史数据构建伤亡人数预测模型,得到死亡及受伤人数平均误差率分别为1.69%、1.53%;采用伤亡人数的影响指标汽车保有量、国内生产总值、公路总里程、人均道路面积的同时段历史数据构建预测模型,得到死伤人数平均误差率分别为1.76%、2.13%;对比发现将DAE运用到公路事故伤亡人数预测精度较高,且采用伤亡人数时间序列数据较影响指标预测精度更高,故使用前者对2016-2020年数据进行预测,得出未来我国公路事故死亡人数将在一定时间内保持平稳,而受伤人数将会明显下降。  相似文献   

18.
The fuzzy logical relationships and the midpoints of interval have been used to determine the numerical in-out-samples forecast in the fuzzy time series modeling. However, the absolute percentage error is still yet significantly improved. This can be done where the linguistics time series values should be forecasted in the beginning before the numerical forecasted values obtained. This paper introduces the new approach in determining the linguistic out-sample forecast by using the index numbers of linguistics approach. Moreover, the weights of fuzzy logical relationships are also suggested to compensate the presence of bias in the forecasting. The daily load data from National Electricity Board (TNB) of Malaysia is used as an empirical study and the reliability of the proposed approach is compared with the approach proposed by Yu. The result indicates that the mean absolute percentage error (MAPE) of the proposed approach is smaller than that as proposed by Yu. By using this approach the linguistics time series forecasting and the numerical time series forecasting can be resolved.  相似文献   

19.
模型输入对模糊神经网络预报模型的影响研究   总被引:1,自引:0,他引:1       下载免费PDF全文
为了探索模型输入对模糊神经网络预报模型预测性能的影响,研究了通过减少预报模型自变量组合的复共线性影响,并结合相似系数计算分析方法建立了一种新的模糊神经网络预报模型。以气象学科的逐日降水预报作为研究对象,利用这种新的模糊神经网络预报模型进行了实际预报试验,并与常规的模糊神经网络预报方法,中国气象局T213数值预报模式以及逐步回归预报方法的预报结果进行了对比分析。结果表明,这种基于条件数和相似系数计算的模糊神经网络预报新方法对49天降水的独立样本预报平均绝对误差为7.33mm,预报误差比模糊神经网络预报模型下降了5.9%,比传统的逐步回归方法下降了14.9%,比中国气象局T213数值预报模式的预报结果下降了13.4%。显示了很好的应用前景。  相似文献   

20.
ABSTRACT

Land degradation in semi-arid natural environments is usually associated with climate vulnerability and anthropic pressure, leading to devastating social, economic and environmental impacts. In this sense, remotely sensed vegetation parameters, such as the Normalized Difference Vegetation Index (NDVI), are widely used in the monitoring and forecasting of vegetation patterns in regions at risk of desertification. Therefore, the objective of this study was to model NDVI time series at six desertification hotspots in the Brazilian semi-arid region and to verify the applicability of such models in forecasting vegetation dynamics. We used NDVI data obtained from the MOD13A2 product of the Moderate Resolution Imaging Spectroradiometer sensor, comprising 16-day composites time series of mean NDVI and NDVI variance for each hotspot during the 2000–2018 period. We also used rainfall measured by weather stations as an explanatory variable in some of the tested models. Firstly, we compared Holt-Winters with Box-Jenkins and Box-Jenkins-Tiao (BJT) models. In all hotspots the Box-Jenkins and BJT models performed slightly better than Holt-Winters models. Overall, model performance did not improve with the inclusion of rainfall as an exogenous explanatory variable. Mean NDVI series were modelled with a correlation of up to 0.94 and a minimum mean absolute percentage error of 5.1%. NDVI variance models performed slightly worse, with a correlation of up to 0.82 and a minimum mean absolute percentage error of 22.0%. After the selection of the best models, we combined mean NDVI and NDVI variance models in order to forecast mean-variance plots that represent vegetation state dynamics. The combined models performed better in representing dry and degraded vegetation states if compared to robust and heterogeneous vegetation during wet periods. The forecasts for one seasonal period ahead were satisfactory, indicating that such models could be used as tools for the monitoring of short-term vegetation states.  相似文献   

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