船用燃气轮机状态趋势预测算法研究

燃气轮机是船舶动力系统的重要组成部分,为了提高船舶性能,需对其进行状态趋势预测;首先研究了常用的几种预测算法,总结了各自优缺点和适用范围,对常见预测方法的技术特点进行了详细分析;为提高船用燃气轮机状态预测精度,提出了组合优选和虚拟预测的思想,通过研究组合预测方法实现了燃汽轮机状态的趋势预测,验证了组合优选和虚拟预测的可操作性和正确性,将预测结果同直接预测相比较,验证了其优越性。     

基于时间序列的航天器遥测数据预测算法

在航天器遥测数据预测领域,基于时间序列的预测方法有着广阔的应用前景;时间序列有一明显的特性就是记忆性,记忆性是指时间数列中的任一观测值的表现皆受到过去观测值影响;它的基本思想是根据观测数据的特点为数据建立尽可能合理的统计模型,利用模型的统计特性解释数据的统计规律,以期达到预报的目的;提出了采用模式识别和参数估计的方法,结合航天器遥测动态数据,建立关于航天器遥测数据的时序预测模型,对航天器遥测数据趋势进行检测和预报。     

时间序列的神经网络预测方法研究

本文利用动态神经网络可以逼近任意范函这一特性，提出利用此种动态神经网络对时间序列进行预测的方法，并利用设计的网络对某些典型的时间序列进行预测，结果表明利用神经网络对时间序列进行预测有较好的逼近效果。     

基于灰色预测的直升机齿轮箱状态预测方法

作为直升机上重要的关键部件,直升机齿轮箱能够将动力转换为动力输出形式,从而满足不同形式下动力的需要。针对直升机齿轮箱状态无法准确预测的技术难题,本文将灰色系统理论中的灰色预测方法运用到直升机齿轮箱中,有效解决了齿轮箱使用状态难以准确预测的技术难题。首先对采集到的直升机齿轮箱的不同的振动信号进行特征提取,然后采用信息融合技术,将不同振动信号的特征值进行融合,最后运用灰色预测方法对直升机齿轮箱的使用状态进行预测。文中对所提出的方法进行了试验验证,结果表明,所提出的基于灰色预测的直升机齿轮箱状态预测方法能够实现对直升机齿轮箱的状态准确预测的效能,并对其他航空设备以及机械设备的状态预测具有一定的借鉴意义。     

混沌时间序列多步自适应预测方法

针对混沌时间序列局域自适应预测方法在多步预测中预测器系数无法调节的问题，根据混沌时间序列的短期可预测性及自适应算法的自适应跟踪混沌运动轨迹的特点，提出了混沌时间序列多步自适应预测方法.仿真结果表明，此方法的多步预测性能明显好于局域自适应预测方法的多步预测性能. 关键词： 多步自适应预测方法 局域自适应预测方法 混沌时间序列     

基于遗传神经网络的旋转机械故障预测方法研究

许多大型旋转机械运行工况恶劣,非平稳、非线性特征明显,以及各种突发性、偶然性因素的影响,给基于振动信号处理的状态预测和状态维护分析带来困难。神经网络以其强大的处理非线性系统的能力在故障预测中得到广泛的应用,但由于其在追求高精度训练目标时易陷入局部极值,且收敛速度慢甚至发散。针对这个问题,提出了采用遗传算法对神经网络连接权值和阈值进行优化,这样不仅发挥了神经网络广泛的映射特性也使遗传算法的全局搜索优势尽显无疑。通过组合这两种算法,在提升网络学习的准确度方面,优点尤其突出,最终提高对旋转机械故障预测和寿命估计的性能,这在某环境模拟试验系统动力风机的轴承磨损故障预测中得到了验证。     

一种预测混沌时间序列的模糊神经网络方法

给出了一种预测混沌时间序列的模糊神经网络及其学习方法，给出的方法能直接从数据中提取模糊规则，经过优化得到最佳模糊规则库，并利用神经网络的自学习功能修改隶属函数的参数和网络的权值，减少了规则的匹配过程，加快了推理速度，增强了网络的自适应能力. 使用该神经网络及其学习方法对Lorenz混沌时间序列进行了预测仿真研究，试验结果表明给出的预测工具和方法是有效的. 关键词： 模糊神经网络 模糊规则提取 混沌时间序列预测     

基于混沌算子网络的时间序列多步预测研究

结合相空间重构理论和时间序列分析理论,提出一种用于时间序列多步预测的网络模型.网络采用多个混沌算子加权求和的形式构成.网络各层单元采用固定权值连接,混沌算子的控制参数利用混沌优化算法进行训练调节,从而控制预测网络的动力学行为.利用已知时间序列数据构造出训练样本,训练样本在网络训练过程中仅使用一次,促使网络的动力学特性随时间的推移而变化,并逐渐逼近被预测系统的动力学特性,最终完成对未来时刻数据的预测.在对理论数据进行预测分析时,通过计算预测序列的Lyapunov指数验证了预测网络的有效性.在对实际时间序列的预测过程中,该网络表现出了良好的预测性能.仿真结果表明,该预测网络可对多种时间序列在一定的预测步长范围内实现有效的预测.     

基于一种新型聚类算法的RBF神经网络混沌时间序列预测

运用两阶段学习方法构建径向基函数(RBF)神经网络模型预测混沌时间序列.在利用非监督学习算法确定网络隐层中心时,提出了一种基于高斯基的距离度量,并联合输入输出聚类的策略.基于Fisher可分离率设计高斯基距离度量中的惩罚因子,可以提高聚类的性能.而输入输出聚类策略的引入,建立了聚类性能与网络预测性能之间的联系.因此,根据本文方法构建的网络模型,一方面可以加快网络训练的速度,另一方面可以提高预测性能.将该方法对Mackey-Glass, Lorenz和Logistic混沌时间序列进行了预测仿真研究,仿真结果表明了该方法的有效性. 关键词： 混沌时间序列 预测 径向基神经网络 聚类     

基于Cholesky分解的增量式RELM及其在时间序列预测中的应用

针对应用于混沌时间序列预测的正则极端学习机(RELM)网络结构设计问题,提出一种基于Cholesky分解的增量式RELM训练算法.该算法通过逐次增加隐层神经元的方式自动确定最佳的RELM网络结构,并以Cholesky分解方式计算其输出权值,有效减小了隐层神经元递增过程的计算代价.混沌时间序列预测实例表明,该算法可有效实现最佳RELM网络结构的自动确定,且计算效率高.利用该算法训练后的RELM预测模型具有预测精度高的优点,适用于混沌时间序列预测. 关键词： 神经网络 极端学习机 混沌时间序列 时间序列预测     

Cancer Research Trend Analysis Based on Fusion Feature Representation

Machine learning models can automatically discover biomedical research trends and promote the dissemination of information and knowledge. Text feature representation is a critical and challenging task in natural language processing. Most methods of text feature representation are based on word representation. A good representation can capture semantic and structural information. In this paper, two fusion algorithms are proposed, namely, the Tr-W2v and Ti-W2v algorithms. They are based on the classical text feature representation model and consider the importance of words. The results show that the effectiveness of the two fusion text representation models is better than the classical text representation model, and the results based on the Tr-W2v algorithm are the best. Furthermore, based on the Tr-W2v algorithm, trend analyses of cancer research are conducted, including correlation analysis, keyword trend analysis, and improved keyword trend analysis. The discovery of the research trends and the evolution of hotspots for cancers can help doctors and biological researchers collect information and provide guidance for further research.     

Institution Publication Feature Analysis Based on Time-Series Clustering

Based on the time series of articles obtained from the literature, we propose three analysis methods to deeply examine the characteristics of these articles. This method can be used to analyze the construction and development of various disciplines in institutions, and to explore the features of the publications in important periodicals in the disciplines. By defining the concepts and methods relevant to research and discipline innovation, we propose three methods for analyzing the characteristics of agency publications: numerical distribution, trend, and correlation network analyses. The time series of the issuance of articles in 30 important journals in the field of management sciences were taken, and the new analysis methods were used to discover some valuable results. The results showed that by using the proposed methods to analyze the characteristics of institution publications, not only did we find similar levels of discipline development or similar trends in institutions, achieving a more reasonable division of the academic levels, but we also determined the preferences of the journals selected by the institutions, which provides a reference for subject construction and development.     

Hierarchical Amplitude-Aware Permutation Entropy-Based Fault Feature Extraction Method for Rolling Bearings

In order to detect the incipient fault of rolling bearings and to effectively identify fault characteristics, based on amplitude-aware permutation entropy (AAPE), an enhanced method named hierarchical amplitude-aware permutation entropy (HAAPE) is proposed in this paper to solve complex time series in a new dynamic change analysis. Firstly, hierarchical analysis and AAPE are combined to excavate multilevel fault information, both low-frequency and high-frequency components of the abnormal bearing vibration signal. Secondly, from the experimental analysis, it is found that HAAPE is sensitive to the early failure of rolling bearings, which makes it suitable to evaluate the performance degradation of a bearing in its run-to-failure life cycle. Finally, a fault feature selection strategy based on HAAPE is put forward to select the bearing fault characteristics after the application of the least common multiple in singular value decomposition (LCM-SVD) method to the fault vibration signal. Moreover, several other entropy-based methods are also introduced for a comparative analysis of the experimental data, and the results demonstrate that HAAPE can extract fault features more effectively and with a higher accuracy.     

基于区域特性量测的图像融合方法

在图像融合过程中，融合规则及融合算子的选择对于融合的质量至关重要，也是图像融合中至今尚未很好解决的难点问题。本文给出了一种新的融合规则——基于区域特性量测的融合规则，即在对某一分解层图像进行融合处理时，为了确定融合后的像素不仅要考虑参加融合图像中对应的各像素，而且要考虑参加融合像素的局部领域。     

A Multi-Method Survey on the Use of Sentiment Analysis in Multivariate Financial Time Series Forecasting

In practice, time series forecasting involves the creation of models that generalize data from past values and produce future predictions. Moreover, regarding financial time series forecasting, it can be assumed that the procedure involves phenomena partly shaped by the social environment. Thus, the present work is concerned with the study of the use of sentiment analysis methods in data extracted from social networks and their utilization in multivariate prediction architectures that involve financial data. Through an extensive experimental process, 22 different input setups using such extracted information were tested, over a total of 16 different datasets, under the schemes of 27 different algorithms. The comparisons were structured under two case studies. The first concerns possible improvements in the performance of the forecasts in light of the use of sentiment analysis systems in time series forecasting. The second, having as a framework all the possible versions of the above configuration, concerns the selection of the methods that perform best. The results, as presented by various illustrations, indicate, on the one hand, the conditional improvement of predictability after the use of specific sentiment setups in long-term forecasts and, on the other, a universal predominance of long short-term memory architectures.     

分形信号算法的CASI高光谱数据岩性特征提取

高光谱数据具有图谱合一和数据量大的特点,数据降维是主要的研究方向。波段选择和特征提取是目前高光谱降维的主要方法,就高光谱数据图像岩性特征提取的方法进行了试验和探讨。基于高光谱影像的自相似特征, 探索了分形信号算法在CASI高光谱数据岩性特征提取上的应用研究。以CASI高光谱影像数据为研究对象, 将基于地毯的方法进行修正后用于计算高光谱影像中每一像元的分形信号值。试验结果表明, 与其他分类算法相比分形信号算法增强高光谱图像的影像特征从另一个侧面更细致的描述了不同光谱的可区分性。分形信号影像在一定程度上可以更好地突出基岩裸露地区岩性特征, 从而可以实现影像地表岩性特征提取的目的。原始光谱曲线自身形态特征、初始尺度的选择以及迭代步长等对分形信号和分形特征尺度均有影响。目前,光谱曲线的分形信号特征研究还不多,对其物理意义和定量分析尚需要深入研究。     

非接触式虹膜图像获取及特征提取方法的研究

报道了一种由使用者自行瞄准来实现虹膜图像非接触式获取的新方法,对获得的图像数据进行了虹膜纹理特征提取试验,验证了方法的可行性和有效性。通过设计几何光照明来辅助使用者定位瞄准,使眼睛处于前后合适的位置;并在摄像机镜头前面放置一片半透半反镜,帮助使用者观察自己眼睛的像来判断眼睛是否偏出摄像机的视场范围。通过该方法可以得到虹膜特征识别的图像数据。采用二维伽博小波对虹膜纹理进行特征提取,并计算两个虹膜特征码值中数值相等的特征位个数,对采集到的虹膜图像进行了特征匹配实验,得到的实验数据证实了该系统完全满足虹膜特征识别的要求。     

Deep Prediction Model Based on Dual Decomposition with Entropy and Frequency Statistics for Nonstationary Time Series

The prediction of time series is of great significance for rational planning and risk prevention. However, time series data in various natural and artificial systems are nonstationary and complex, which makes them difficult to predict. An improved deep prediction method is proposed herein based on the dual variational mode decomposition of a nonstationary time series. First, criteria were determined based on information entropy and frequency statistics to determine the quantity of components in the variational mode decomposition, including the number of subsequences and the conditions for dual decomposition. Second, a deep prediction model was built for the subsequences obtained after the dual decomposition. Third, a general framework was proposed to integrate the data decomposition and deep prediction models. The method was verified on practical time series data with some contrast methods. The results show that it performed better than single deep network and traditional decomposition methods. The proposed method can effectively extract the characteristics of a nonstationary time series and obtain reliable prediction results.