TCL: a taxi trajectory prediction model combining time and space features

Vehicle trajectory modeling is an important foundation for urban intelligent services. Trajectory prediction of cars is a hot topic. A model including convolutional neural network (CNN) and long short-term memory (LSTM) was proposed, which is named trajectory-CNN-LSTM (TCL). CNN can extract the spatial features of the trajectory in the input image. Besides, LSTM can extract the time-series features of the input trajectory. After that, the model uses fully connected layers to merge the two features for the final predicting. The experiments on the Porto dataset of The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) show that the average prediction error of TCL is reduced by 0.15 km, 0.42 km, and 0.39 km compared to the trajectory-convolution (T-CONV), multi-layer perceptron (MLP), and recurrent neural network (RNN) model, respectively.     

基于遗传算法优化神经网络的藻类繁殖状态软测量方法

为了提高BP神经网络模型对海洋藻类生长状态软测量的准确性,提出了一种基于遗传优化算法优化BP神经网络的软测量方法.利用遗传算法优化BP神经网络的权值和阈值,然后训练BP神经网络预测模型以求得最优解,再将该预测结果与传统BP网络预测模型的预测结果进行对比.对仿真结果进行有效性验证后,结果表明,通过这种软测量方法,经遗传算法优化后的BP神经网络可以在更短的时间里创造更高的预测准确性,大大提高了对海洋藻类生长状态预测的效率.     

On the use of a scanpath predictor and convolutional neural network for blind image quality assessment

Image quality assessment is an important field in computer vision, since it has a great impact on related tasks. To meet these needs, a plethora of metrics has been developed. In this paper, we propose an efficient method that estimates the quality of 2D images without access to the pristine image. This metric is modeled based on the relevant patches selected by saliency information and a convolution neural network. To exploit the saliency information, only the more perceptually relevant patches that impact subjective judgment more, are considered. To this end, we first compute the saliency map of the distorted image. Then, a scanpath predictor that aims to mimic the visual behavior is employed as patch selector. Finally, a CNN model is used to predict the quality score through the extracted patches. To the best of our knowledge this is the first study to associate a scanpath prediction method and CNN to assess the quality of 2D images. Four CNN models were compared (AlexNet, VGG16, VGG19 and ResNet50) and the performance of the best CNN was compared to the state-of-the-art on four datasets. Experimental results demonstrated the efficiency of the proposed approach and its generalization capacity.     

基于神经网络方法预测薄膜的厚度

首次利用前馈三层神经网络模型 ,建立了场发射薄膜的膜厚的神经网络预测模型 ,用金刚石薄膜的膜厚数据进行验证 .结果表明 ,该模型预测的相对误差小于 6.1 % .     

Forecasting Method of Stock Market Volatility in Time Series Data Based on Mixed Model of ARIMA and XGBoost

Stock price forecasting is an important issue and interesting topic in financial markets.Because reasonable and accurate forecasts have the potential to generate high economic benefits,many researchers have been involved in the study of stock price forecasts.In this paper,the DWT-ARIMAGSXGB hybrid model is proposed.Firstly,the discrete wavelet transform is used to split the data set into approximation and error parts.Then the ARIMA(0,1,1),ARIMA(1,1,0),ARIMA(2,1,1)and ARIMA(3,1,0)models respectively process approximate partial data and the improved xgboost model(GSXGB)handles error partial data.Finally,the prediction results are combined using wavelet reconstruction.According to the experimental comparison of 10 stock data sets,it is found that the errors of DWT-ARIMA-GSXGB model are less than the four prediction models of ARIMA,XGBoost,GSXGB and DWT-ARIMA-XGBoost.The simulation results show that the DWT-ARIMA-GSXGB stock price prediction model has good approximation ability and generalization ability,and can fit the stock index opening price well.And the proposed model is considered to greatly improve the predictive performance of a single ARIMA model or a single XGBoost model in predicting stock prices.     

Computational Intelligence Prediction Model Integrating Empirical Mode Decomposition,Principal Component Analysis,and Weighted k-Nearest Neighbor

On the basis of machine leaning, suitable algorithms can make advanced time series analysis. This paper proposes a complex k-nearest neighbor (KNN) model for predicting financial time series. This model uses a complex feature extraction process integrating a forward rolling empirical mode decomposition (EMD) for financial time series signal analysis and principal component analysis (PCA) for the dimension reduction. The information-rich features are extracted then input to a weighted KNN classifier where the features are weighted with PCA loading. Finally, prediction is generated via regression on the selected nearest neighbors. The structure of the model as a whole is original. The test results on real historical data sets confirm the effectiveness of the models for predicting the Chinese stock index, an individual stock, and the EUR/USD exchange rate.     

改进的BP算法在股市预测中的应用

股票价格预测是证券界和学术界的一个重要的研究课题。神经网络具有强大的非线性逼近能力,文中采用MO-VLBP神经网络建立股票价格预测模型,对某银行的收盘价进行预测。实验结果证明,MO-VLBP网络模型应用于股票价格的短期预测,运算速度快、预测精度高。     

基于双激活层深度卷积特征的人脸美丽预测研究

目前,人脸美丽预测存在数据规模小、分类难度大、深度特征研究不足等问题.为此,本文提出基于双激活层深度卷积特征的人脸美丽预测研究的解决方案.首先,采用数据增强和人脸对齐方法来增加训练集的样本数量和提高数据库的数据质量.其次,提出一种双激活层改进CNN模型,使其更适合人脸美丽预测应用.实验结果表明,本文所提方法在分类和回归预测方面均大幅度优于传统人脸美丽预测方法;同时,在主流的CNN模型中取得了较好的实时性和准确性,基于2000测试集的分类准确率达到61.1%,回归相关度达到0.8546.因此,双激活层在深层人脸美丽特征学习中发挥了重要作用,可广泛应用于人脸图像识别与处理.     

基于正则化RBF神经网络的混凝土强度预测

针对目前混凝土28天强度值的预测需时长、精度低的现状,建立了基于正则化RBF神经网络的混凝土强度预测模型,并运用MATLAB7．13进行仿真实验。实验结果表明该模型综合考虑了影响混凝土强度的各种因素,能够实现非线性关系,具有较高的预测精度,并且训练速度快,可以节约大量的时间、人力、物力和财力,在混凝土强度预测领域具有广泛的应用前景。     

一种基于组合神经网络的HEVC帧间预测方法

高效视频编码(HEVC)作为最新视频编码标准,有着非常高的压缩效率,但是由于各种新技术的提出,其编码复杂度也大大提高。复杂度对视频编码有着重要意义,低复杂度编码的研究非常必要。利用神经网络进行HEVC的分区预测为低复杂度编码提供了有效的解决方案。文中提出了一种基于卷积神经网络(CNN)和长短期记忆网络(LSTM)的组合网络架构来对帧间分区进行预测的方法,利用自建数据库对网络进行训练;文中设计了一种预搜索模块来建立训练数据库,仿真结果表明,神经网络的精度可达87%,利用该网络架构进行帧间预测可以实现52%~71%的复杂度节省。     

中国东部典型城市群AOD时空演变及预测

为精准预测我国东部典型城市群的气溶胶光学厚度(AOD),基于2010-2019年MODIS数据,分析了京津冀、长三角、珠三角区域之间以及区域内部的AOD时空差异特征,构建了小波变换与BP神经网络相结合的AOD预测模型,并对典型城市群AOD进行了预测.研究结果表明:1)各城市群气溶胶浓度峰值均出现在夏季,京津冀地区AOD...     

瓦斯突出多维预测模型的研究

煤矿瓦斯灾害是煤矿生产中最危险的事故,给安全生产带来严重隐患,所以瓦斯灾害的预测很重要。该文针对以往的瓦斯预测法无法解决多维灾害问题,提出了多维瓦斯预测模型和自回归人工神经网络形成一种新的自适应预测方法,对多维灾害进行预测。实验结果表明,自回归神经网络预测精度高,自适应性强,可对瓦斯灾害趋势做出很好的预测。     

基于BP神经网络的电视节目收视率预测模型

电视节目收视率预测是一种典型非线性预测,收视率在短时间内相对稳定。人工神经网络具有良好的容错性、自适应学习能力以及非线性映射能力,采用人工神经网络做收视率预测精度较高。基于BP神经网络建立了预测模型,并采用软件仿真的方式对预测过程以及预测结果进行分析,实验结果表明采用BP神经网络预测电视节目收视率是可行的。     

Futuristic speed prediction using auto‐regression and neural networks for mobile ad hoc networks

In this paper, we propose a speed prediction model using auto‐regressive integrated moving average (ARIMA) and neural networks for estimating the futuristic speed of the nodes in mobile ad hoc networks (MANETs). The speed prediction promotes the route discovery process for the selection of moderate mobility nodes to provide reliable routing. The ARIMA is a time‐series forecasting approach, which uses autocorrelations to predict the future speed of nodes. In the paper, the ARIMA model and recurrent neural network (RNN) trains the random waypoint mobility (RWM) dataset to forecast the mobility of the nodes. The proposed ARIMA model designs the prediction models through varying the delay terms and changing the numbers of hidden neuron in RNN. The Akaike information criterion (AIC), Bayesian information criterion (BIC), auto‐correlation function (ACF), and partial auto‐correlation function (PACF) parameters evaluate the predicted mobility dataset to estimate the model quality and reliability. The different scenarios of changing node speed evaluate the performance of prediction models. Performance results indicate that the ARIMA forecasted speed values almost match with the RWM observed speed values than RNN values. The graphs exhibit that the ARIMA predicted mobility values have lower error metrics such as mean square error (MSE), root MSE (RMSE), and mean absolute error (MAE) than RNN predictions. It yields higher futuristic speed prediction precision rate of 17% to 24% throughout the time series as compared with RNN. Further, the proposed model extensively compares with the existing works.     

预测轨迹在真实环境中的修正技术研究

在4D轨迹预测模型结果实际工程应用的过程中,总是存在飞行计划预测轨迹和雷达航迹不一致的现象,通过对模型和实际数据的分析,查找计划预测轨迹的偏离因素。该文描述了实际运行过程中涉及影响预测轨迹的各类因素,分析了数据对模型的影响;并结合分析结果提出了实施过程中对计划轨迹的修正方法,该分析结果将促进4D预测轨迹在空中交通管理领域中的实施。     

遗传算法优化BP神经网络的大功率LED结温预测

将遗传算法(GA)与BP神经网络相结合,对研发的120W LED双进双出的射流冲击水冷散热系统中LED阵列的结温进行预测。采用GA优化BP网络的权值和阈值,利用BP算法训练网络,改善了单独使用BP网络容易陷入局部极小值和收敛速度慢的缺点。并且在训练过程中为了使网络输出有足够长的空间,改进了GA的数据处理。结果表明,经GA优化的BP神经网络较使用Levenberg-Marquardt(LM)算法优化的BP神经网络的大功率LED结温预测精确度提高了14.14%,且预测效果较稳定。GA和BP神经网络相结合的结温预测模型较传统的结温测量方法更能掌握散热结构设计的主动性,对大功率LED寿命的延长有较高的实用价值。     

毫米波电路引线楔焊质量智能预测技术研究

针对毫米波电路引线楔形焊接工艺优化难题,提出将一种带惩罚函数项的改进BP (Back Propagation,反向传播)神经网络算法用于引线楔形焊接质量智能预测中.通过试验分析了影响楔形焊接质量的关键工艺参数,提取了楔形焊接质量评价指标,基于改进的BP神经网络,建立了引线楔焊质量智能预测模型.研究结果表明,所提出的改进...     

基于改进编解码网络的极化SAR地物分类

基于实数域的卷积神经网络(CNN)模型无法充分利用极化合成孔径雷达(PolSAR)图像丰富的相位信息,并且逐像素切片预测存在大量冗余计算,导致分类效率低下.针对以上问题,本文提出一种改进编解码网络模型.首先构建复数域CNN模型,并进行低采样率下的模型训练;然后构建复数域双通道编解码网络模型,引入改进空洞空间金字塔池化(...     

基于情感神经网络的风电功率预测

风力发电功率预测对于风能并网具有重要意义.采用一种可用于复杂系统和模式建模的新型神经网络——情感神经网络,对风力发电功率进行预测.为防止ENN在训练时陷入局部最优解,提出采用遗传算法对其进行训练.采用预测误差的均方根和标准差衡量预测准确性、稳定性,对ENN性能进行了检验.结果表明,相比于人工神经网络、支持向量机和自滑动回归模型,ENN能够获得更高的预测准确率和预测可靠性.     

No-reference image quality assessment based on hybrid model

The aim of research on the no-reference image quality assessment problem is to design models that can predict the quality of distorted images consistently with human visual perception. Due to the little prior knowledge of the images, it is still a difficult problem. This paper proposes a computational algorithm based on hybrid model to automatically extract vision perception features from raw image patches. Convolutional neural network (CNN) and support vector regression (SVR) are combined for this purpose. In the hybrid model, the CNN is trained as an efficient feature extractor, and the SVR performs as the regression operator. Extensive experiments demonstrate very competitive quality prediction performance of the proposed method.