基于多元数据特征和改进随机森林的智能配电网异常数据辨识

智能配电网异常数据的准确辨识对于提高电网安全运行和调度具有重要意义。本文提出一种基于多元数据特征和改进随机森林算法的异常数据辨识方法。首先,在分析异常数据辨识过程的基础上,利用k-means、箱线图法等提取原始数据异常特征;考虑配电网技术需求,挖掘电网运行的衍生特征。然后,针对类不平衡问题提出结合过采样方法的混合Bootstrap抽样和加权投票策略,引入信息增益率优化最优特征选择,增加算法稳定性。最后,仿真分析了决策树数量和衍生特征对算法辨识性能的影响,并与支持向量机、神经网络等算法进行性能比较。实验结果表明本文方法有效、合理,具有优异的辨识性能和效率。     

基于误差修正的短期光伏功率预测

随着太阳能使用率的不断提高,太阳能资源的易变性使电网管理出现了困难。为了提高太阳能发电功率的预测精度,在安全稳定运行中保障工业电力系统的正常使用,提出一种结合改进的自适应噪声互补集成经验模态分解(improved complementary ensemble empirical mode decomposition with adaptive noise, ICEEMEDAN)与差分自回归移动平均(autoregressive integrated moving average, ARIMA)的长短期记忆(long-term and short-term memory, LSTM)神经网络模型,使用该复合模型对光伏发电功率进行预测,通过对影响光伏功率的不同变量进行评估,获取特征重要性并作为模型的输入,在时间尺度上利用改进后的LSTM模型进行建模,以R²等相关指标评判模型性能的优劣,从而实现完整的光伏功率预测。实验采用澳大利亚光伏数据集群(DKASC)中一光伏电站2016年实测数据进行验证,两组实验中修正预测模型较单一LSTM神经网络光伏功率预测模型的精确度分别提高...     

基于正交拉丁方多位纠错算法的数据可靠性存储设计

地震勘探过程中采集点多数据量大,面对海量数据的存储与转发,需要配置安全性可靠性更高的存储设备与存储方法,以系统级加固为基础,设计了基于ECC(error correction codes)代理的大数逻辑可解正交拉丁方(OLS)编码方法,为每16个数据添加16个奇偶校验位,通过构建校验矩阵来纠正多位翻转错位。经过ZYNQ7000 FPGA平台的设计及仿真验证,排除了单事件扰乱、实现了多功能扰乱控制电路中单事件中断的保护、及单双错误的纠正。     

一种基于梯度分块的直方图平移可逆信息隐藏

针对大多数基于直方图平移的可逆信息隐藏中载体图像分块方法过于粗糙的问题,根据相邻像素之间具有相似特性,提出对载体图像按像素梯度进行分割,将图像分为平滑区域和粗糙区域。同时为减少像素的无效平移,只对平滑区域分块并采用直方图的次峰值点进行双向移位实现信息可逆隐藏,同时将零值点嵌入图像;不需要单独传送峰值点和零值点信息,降低了通信负担。实验表明,该方法在图像质量和峰值信噪比上具有明显的提升,可应用于保密传输等领域。     

VP型宽频带倾斜仪故障信号的BBA-SOM智能诊断

针对现有VP型倾斜仪故障诊断主要依靠人工经验和诊断流程较为复杂的问题,提出以互补集合经验模态分解(complete ensemble empirical mode decomposition, CEEMD)多尺度近似熵和二进制蝙蝠算法(binary bat algorithm, BBA)优化SOM神经网络参数的VP型倾斜仪故障诊断新方。首先,将归一化后的仪器故障信号进行CEEMD分解,对6阶本征模态函数(intrinsic mode function, IMF)求取多尺度近似熵值;然后将网络输入法按比例分为训练集和测试集,以训练集的识别率为适应度函数,应用二进制蝙蝠算法(binary bat algorithm, BBA)优化SOM神经网络的竞争层维数和网络训练次数;最后应用上述得到的BBA-SOM网络模型对倾斜仪故障特征数据进行辨识。实验表明：CEEMD多尺度近似熵判据对倾斜仪故障特征的区分效果符合预期;相对于朴素贝叶斯、AdaBoost集成学习与LDA等学习模型,BBA-SOM模型可以准确进行故障诊断;该方法对实现VP型倾斜仪故障的自动诊断有重要现实意义。     

基于ARIMA-LSTM-XGBoost组合模型的铁路货运量预测

为提升铁路货运量预测精度和泛化能力,综合考虑铁路货运量时间序列数据的线性和非线性特征,提出了基于ARIMA-LSTM-XGBoost组合模型的铁路货运量预测方法。首先使用ARIMA模型对我国铁路货运量进行初步预测,再利用LSTM网络对残差进行校正,并将其与XGBoost模型结合,采用误差倒数法确定权重,构建一种加权组合模型。最后将组合模型与ARIMA、ARIMA-LSTM、LSTM、XGBoost模型进行对比,借助均方误差(MSE)、均方根误差(RMSE)、平均绝对值误差(MAE)、平均绝对百分比误差(MAPE)对上述模型的预测精度进行对比分析。使用2007年-2021年全国铁路货运量月度数据进行实验,实验结果表明：组合模型的MSE、RMSE、MAE、MAPE分别为0.011 9、0.109 4、0.068 3、1.775 2%,预测误差均低于上述对比模型,模型的预测精度和泛化能力都有所提升。     

Neural model identification,variable selection and model adequacy

In recent years an impressive array of publications has appeared claiming considerable successes of neural networks in modelling financial data but sceptical practitioners and statisticians are still raising the question of whether neural networks really are ‘a major breakthrough or just a passing fad’. A major reason for this is the lack of procedures for performing tests for misspecified models, and tests of statistical significance for the various parameters that have been estimated, which makes it difficult to assess the model's significance and the possibility that any short‐term successes that are reported might be due to ‘data mining’. In this paper we describe a methodology for neural model identification which facilitates hypothesis testing at two levels: model adequacy and variable significance. The methodology includes a model selection procedure to produce consistent estimators, a variable selection procedure based on statistical significance and a model adequacy procedure based on residuals analysis. We propose a novel, computationally efficient scheme for estimating sampling variability of arbitrarily complex statistics for neural models and apply it to variable selection. The approach is based on sampling from the asymptotic distribution of the neural model's parameters (‘parametric sampling’). Controlled simulations are used for the analysis and evaluation of our model identification methodology. A case study in tactical asset allocation is used to demonstrate how the methodology can be applied to real‐life problems in a way analogous to stepwise forward regression analysis. Neural models are contrasted to multiple linear regression. The results indicate the presence of non‐linear relationships in modelling the equity premium. Copyright © 1999 John Wiley & Sons, Ltd.     

Economic factors and the stock market: a new perspective

It has been widely accepted that many financial and economic variables are non‐linear, and neural networks can model flexible linear or non‐linear relationships among variables. The present paper deals with an important issue: Can the many studies in the finance literature evidencing predictability of stock returns by means of linear regression be improved by a neural network? We show that the predictive accuracy can be improved by a neural network, and the results largely hold out‐of‐sample. Both the neural network and linear forecasts show significant market timing ability. While the switching portfolio based on the linear forecasts outperforms the buy‐and‐hold market portfolio under all three transaction cost scenarios, the switching portfolio based on the neural network forecasts beats the market only if there is no transaction cost. Copyright © 1999 John Wiley & Sons, Ltd.     

An intelligent business forecaster for strategic business planning

In this paper an intelligent business forecaster for strategic business planning is presented. The forecaster is basically a multi‐layered fuzzy rule‐based neural network which integrates the basic elements and functions of a traditional fuzzy logic inference into a neural network structure. It has also been shown to be superior to two commercially available business forecasters in terms of learning speed and forecasting accuracy. This paper presents the architectural design of the intelligent business forecaster and the results of a study that has been carried out to compare its performance with that of the others. Copyright © 1999 John Wiley & Sons, Ltd.     

基于融合理论的网络在线智能故障诊断模型

针对复杂设备的在线故障诊断问题,依据认知科学和信息融合理论,基于网络环境,提出了一种新的网络在线智能故障诊断模型·它采用定性与定量、局部与综合诊断相结合的诊断策略,即首先进行快速定性诊断,一旦系统异常,立即启动集成神经网络组对来自设备多侧面的故障特征信息进行定量分析和分类,并由专家系统模块对神经网络组的推理过程进行定性解释,最后以D S证据推理模块在全局融合中心实现对各子网络的会诊,提高了诊断的精度和可靠性·作者所开发的丰满水电数字仿真系统的成功应用,验证了模型的有效性和实际应用价值·