特征提取与两种BP算法在入侵检测中的对比

针对海量的等保测评数据,如何从这些数据中选取适量的数据进行入侵行为分析,提出了根据预测变量对预测目标变量的重要性的特征提取方法。该方法采用importance指数来对预测变量进行等级划分。并选取了一些预处理后的数据运用了两种BP算法--标准BP算法和学习速率自适应调整算法进行了系统仿真预测。通过KDDCup99数据集测试表明,后者相对于前者,其学习训练次数大大降低,学习能力和预测准确率明显提高。     

特征提取与两种BP算法在入侵检测中的对比

针对海量的等保测评数据,如何从这些数据中选取适量的数据进行入侵行为分析,提出了根据预测变量对预测目标变量的重要性的特征提取方法。该方法采用importance指数来对预测变量进行等级划分。并选取了一些预处理后的数据运用了两种BP算法——标准BP算法和学习速率自适应调整算法进行了系统仿真预测。通过KDDCup99数据集测试表明,后者相对于前者,其学习训练次数大大降低,学习能力和预测准确率明显提高。     

时空序列预测方法综述

随着数据采集技术的进步,带有地理位置信息的时空数据迅速增长,迫切需要探索有效的时空数据建模方法。时空序列预测是时空数据建模的基础方法之一,它广泛应用于很多领域。目前缺乏对它进行综述的中文文献,因而对这些方法进行归纳和总结具有重要的研究意义。针对时空序列预测问题进行了研究,首先回顾了其应用背景和发展历程,介绍了它的相关定义及特点。然后按其类别介绍了传统的时空序列预测方法、基于传统机器学习的时空序列预测方法和基于深度学习的时空序列预测方法,并分析了这些方法的应用范围和优缺点。最后对时空序列预测未来的研究方向进行了梳理和展望,为研究者们进一步深入研究时空序列预测问题奠定了理论基础。     

基于尖峰消除及小波平滑的新型电力负荷数据清洗策略的研究

短期功率负荷预测是对电力系统进行优化调度的重要参考依据。但是在实际电力负荷预测中,取得的数据会存在大量的噪声以及错误记录,若直接使用这些数据进行模型训练会大大降低预测精度。因此对原始数据进行适当的清洗操作对提高数据的可用性、提高预测精度显得十分重要。研究提出了基于尖峰消除及小波平滑的电力负荷数据清洗策略,可以显著提高电力负荷数据的水平平滑性和垂直平滑性。使用实际负荷数据的SVM及NNet预测结果表明,使用该清洗策略后的预测精度相比于使用前有明显提高。     

基于模糊PA算法的微博信息传播分享预测研究

微博已经成为网民信息获取、分享的主要平台之一。对信息分享进行预测, 是对微博信息传播进行监管控制的基础。微博用户和信息属性中包含着用户偏好、生理特征、内容类型等数据, 基于这些数据可进行信息分享预测。分析了微博信息传播模式、分享预测理论方法, 基于PA算法提出了信息分享预测模型, 以新浪微博数据为例验证了预测模型。结果表明, 该模型对信息分享具有较高的预测准确率。     

基于EMD的仪器稳定度组合预测

为更好地把握仪器的性能变化规律,规避其可能带来的各种风险,将预测理论应用到仪器稳定度预测中,建立了一种基于EMD-SVM的稳定度组合预测模型。首先利用EMD方法对稳定度数据进行分解,然后对分解得到的数据选择一种预测模型进行预测,最后再把所有这些分解数据的预测结果输入到SVM中进行组合预测。通过与移动平均模型、自回归积分滑动平均(ARIMA)模型和线性组合预测模型的预测结果相比较,验证了该方法的有效性。     

基于稀疏表示的数据流异常数据预测方法

为了提高数据流中异常数据的预测速度与精度,提出一种基于稀疏表示的数据流异常数据预测方法。结合了小波噪声检测方法,采用新近发展起来的稀疏表示工具,对含有异常数据的数据流进行小波变换,并得到一组小波系数,然后对这些系数建立稀疏表示模型。引入随机测量矩阵对小波系数进行变换,恢复小波系数的稀疏性达到预测异常数据的目的。仿真结果表明,在一定条件下该方法可获得相当好的预测效果。     

离群数据挖掘方法在电力负荷预测中的应用

根据负荷预测的理论,通过历史数据为基础进行电力负荷数据预测。由于实际运行过程中,采集数据存在错误,使得获得到的负荷预测曲线包含较大的锯齿状。提出一种新的离群数据挖掘方法,即求二直线的夹角方法寻找尖锐点,离群数据为尖锐点处对应电力负荷有功值,然后使用曲线平滑的方法对这些离群数据进行了处理。实验证明,运用提出的这一新的离群数据挖掘方法处理负荷预测曲线,预测结果明显改进。     

基于QAR数据的民用飞机故障预测及验证

飞机飞行过程中产生成百上千种飞行参数和数量庞大的飞行数据,但目前这些数据并没有得到充分有效的利用,飞机的维修还处在以定期维修和事后维修为主的阶段。随着航空技术的不断发展,利用飞行数据进行故障预测,转变民机维修模式向视情维修发展变得越来越有必要。首先对基于QAR数据的民用飞机故障预测技术路线进行了说明。其次介绍了适用于民机QAR数据的两种故障预测方法,包括基于曲线拟合的性能预测方法和基于时间序列的趋势预测方法。再次,详细描述了基于QAR数据的故障预测系统的实现途径。通过预测关键参数变化趋势,达到提前发现故障,以制定合理的维护计划,确保飞行安全的目的。最后采用提出的方法对波音飞机的空调、滑油系统关键参数数据进行预测,预测结果验证了方法的有效性。     

在线拍卖商品最终成交价格预测

在线拍卖产生了大量的电子数据，充分利用这些数据，无论对于卖者、买者还是站点管理员，都会产生极大的作用。卖者通过预测最终的成交价格可以优化自己拍卖商品的拍卖价格。在线拍卖中的大量属性是动态变化的，以至于不能直接运用机器学习算法对最终成交价格进行预测。为了解决这个问题，收集了大量的在线拍卖成交数据，提取和构造了维度固定的特征列表，运用BP算法进行最终成交价格的预测，并对预测结果进行了分析。     

Combining time-series and textual data for taxi demand prediction in event areas: A deep learning approach

Accurate time-series forecasting is vital for numerous areas of application such as transportation, energy, finance, economics, etc. However, while modern techniques are able to explore large sets of temporal data to build forecasting models, they typically neglect valuable information that is often available under the form of unstructured text. Although this data is in a radically different format, it often contains contextual explanations for many of the patterns that are observed in the temporal data. In this paper, we propose two deep learning architectures that leverage word embeddings, convolutional layers and attention mechanisms for combining text information with time-series data. We apply these approaches for the problem of taxi demand forecasting in event areas. Using publicly available taxi data from New York, we empirically show that by fusing these two complementary cross-modal sources of information, the proposed models are able to significantly reduce the error in the forecasts.     

The study and application of a novel hybrid system for air quality early-warning

Air quality early-warning plays a vital role in improving air quality and human health, especially multi-step ahead air quality early-warning, which is significant for both citizens and environmental protection departments. However, most previous studies have only employed simple data decomposition to perform one-step forecasting and were aimed at enhancing forecasting accuracy or stability. Little research has improved these two standards simultaneously, leading to poor forecasting performance. Because of its significance, relevant research focused on multi-step ahead air quality early-warning is especially needed. Therefore, in this paper, a novel hybrid air quality early-warning system, which consists of four modules: data preprocessing module, optimization module, forecasting module and evaluation module, is proposed to perform multi-step ahead air quality early-warning. In this system, an effective data decomposition method called the modified complete ensemble empirical mode decomposition with adaptive noise is developed to effectively extract the characteristics of air quality data and to further improve the forecasting performance. Moreover, the hybrid Elman neural network model, optimized by the multi-objective salp swarm algorithm, is successfully developed in the forecasting module and simultaneously achieves high forecasting accuracy and stability. In addition, the evaluation module is designed to conduct a reasonable and scientific evaluation for this system. Three cities in China are employed to test the effectiveness of the proposed early-warning system, and the results reveal that the proposed early-warning system has superior ability in both accuracy and stability than other benchmark models and can be used as a reliable tool for multi-step ahead air quality early-warning.     

A comparison of neural networks and adaptive neuro-fuzzy inference systems for the prediction of water diffusion through carbon nanotubes

Given the fact that artificial intelligence tools such as neural network and fuzzy logic are capable of learning and inferencing from the past to capture the patterns that exist in the data, this study presents an intelligent method for the forecasting of water diffusion through carbon nanotubes where predictions are generated from neuro-fuzzy structures using molecular dynamics data. Therefore, this research was mainly focused on combining molecular dynamics with artificial intelligence methods in order to reduce the computational time of biomolecular and nanofluidic simulations. Two different artificial intelligence methods are applied for the time-dependent water diffusion forecasting: artificial neural network (ANN) and adaptive neuro-fuzzy inference systems (ANFISs). The effects of different sizes of training sample sets on forecasting performance of ANN and ANFIS are investigated as well. Four different evaluation methods are used to measure the performance and forecasting accuracy of these two methods. As a result, ANFIS presents the higher accuracy than neural network method based on the comparison of these different evaluation methods adopted in this research. The results reported in this research demonstrate that combining of molecular dynamics with artificial intelligence methods can be one of the most powerful and beneficial tools for prediction of important nanofluidic parameters.     

基于XGBOOST-DNN的中期电力负荷预测

精准的负荷预测是电力工作者重要的工作之一,而负荷预测以预测周期的不同,一般可以划分为短期电力负荷预测与中长期电力负荷预测.其中中长期电力负荷预测相较短期电力负荷预测而言,该领域缺乏大量前沿工作者的探索.因此本文提出一种可应用于中期电力负荷预测领域且基于XGBoost-DNN的算法.该算法将树模型和深度神经网络相结合,并将短期电力负荷预测引入到了中期电力负荷预测的工作中,基于树模型自身特点,将数据特征加工成高阶的交叉特征,同时结合原有数据利用深度神经网络可学习到丰富的特征信息.这里是以2017全球能源预测竞赛的数据进行算法分析,其中实验表明,在中期电力负荷预测领域,该方法提出的XGBoost-DNN模型相较于DNN,LSTM而言,其具备更加精准的准确性.     

An integrated fuzzy time series forecasting system

A number of fuzzy time series models have been designed and developed during the last decade. One problem of these models is that they only provide a single-point forecasted value just like the output of the crisp time series methods. In addition, these models are suitable for forecasting stationary or trend time series, but they are not appropriate for forecasting seasonal time series. Hence, the objective of this study is to develop an integrated fuzzy time series forecasting system in which the forecasted value will be a trapezoidal fuzzy number instead of a single-point value. Furthermore, this system can effectively deal with stationary, trend, and seasonal time series and increase the forecasting accuracy. Two numerical data sets are selected to illustrate the proposed method and compare the forecasting accuracy with four fuzzy time series methods. The results of the comparison show that our system can produce more precise forecasted values than those of four methods.     

基于数据预处理灰色神经网络组合和集成预测

当研究的系统扰动因素过大或系统行为在某个时川点发生突变，出现严重扰动系统的异常数据时，提出不应直接按原始数据建模预测，而应根椐实际情况适当地对数据预处理．提出了基于数据修正的改进型灰色神经网络组合和集成预测，并根据南昌火车站旅客发送量时间序列建立了多个模型，从模型预测效果对比中说明数据修正、改进型灰色模型和改进型灰色神经网络、灰色神经网络组合和集成确实能提高预测精度．另外，修正数据要把握一个度，不能修正全部数据，只能修正较异常的数据，要在数据的趋势性和预测的灵敏性间取得平衡。     

HYBRID GREY RELATIONAL ARTIFICIAL NEURAL NETWORK AND AUTO REGRESSIVE INTEGRATED MOVING AVERAGE MODEL FOR FORECASTING TIME-SERIES DATA

The aim of this study is to develop a new hybrid model by combining a linear and nonlinear model for forecasting time-series data. The proposed model (GRANN_ARIMA) integrates nonlinear grey relational artificial neural network (GRANN) and a linear autoregressive integrated moving average (ARIMA) model by combining new features and grey relational analysis to select the appropriate inputs and hybridization succession. To validate the performance of the proposed model, small and large scale data sets are used. The forecasting performance is compared with several models, and these include: individual models (ARIMA, multiple regression, GRANN), several hybrid models (MARMA, MR_ANN, ARIMA_ANN), and an artificial neural network (ANN) trained using a Levenberg Marquardt algorithm. The experiments have shown that the proposed model has outperformed other models with 99.5% forecasting accuracy for small-scale data and 99.84% for large-scale data. The obtained empirical results have proven that the GRANN_ARIMA model can provide a better alternative for time-series forecasting due to its promising performance and capability in handling time-series data for both small- and large-scale data.     

A Rolling Grey Model Optimized by Particle Swarm Optimization in Economic Prediction

Grey system theory has been widely used to forecast the economic data that are often nonlinear, irregular, and nonstationary. Current forecasting models based on grey system theory could adapt to various economic time series data. However, these models ignored the importance of the model parameter optimization and the use of recent data, which lead to poor forecasting accuracy. In this article, we propose a novel forecasting model, called particle swarm optimization rolling grey model (PSO‐RGM(1,1)), based on a rolling mechanism GM with optimized parameters by using the particle swarm optimization algorithm. The simple model is shown to be very effective in forecasting the tertiary industry data sequences, which are short and noisy but regular in secular trend. The experimental results show that PSO‐RGM(1,1) outperforms other commonly used forecasting models on three real economic data sets. Our empirical study shows that PSO is found to be the best overall algorithm to optimize the parameter of RGM compared with other well‐known metaheuristics. Furthermore, we evaluated other variant PSOs and found that single particle PSO outperforms others overall in terms of prediction accuracy, convergence speed, and degree of certainty.     

基于知识发现的电力负荷预测模型

负荷预测是电力系统的一个传统研究问题。针对黑龙江省的气象和经济特点,提出了基于知识发现的负荷预测模型。首先通过传统的近大远小方法生成基本预测曲线,并根据从历史气象资料及负荷数据中提取的规则加以修正,生成最终预测曲线。该模型在黑龙江省电网公司得以应用,收到了较为理想的效果。     

A Long Memory Pattern Modelling and Recognition System for Financial Time-Series Forecasting

In this paper, the concept of a long memory system for forecasting is developed. Pattern modelling and recognition systems are introduced as local approximation tools for forecasting. Such systems are used for matching the current state of the time-series with past states to make a forecast. In the past, this system has been successfully used for forecasting the Santa Fe competition data. In this paper, we forecast the financial indices of six different countries, and compare the results with neural networks on five different error measures. The results show that pattern recognition-based approaches in time-series forecasting are highly accurate, and that these are able to match the performance of advanced methods such as neural networks. Received: 2 April 1998?Received in revised form: 1 February 1999?Accepted: 16 February 1999