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1.
ARIMA is seldom used in supply chains in practice. There are several reasons, not the least of which is the small sample size of available data, which restricts the usage of the model. Keeping in mind this restriction, we discuss in this paper a state-space ARIMA model with a single source of error and show how it can be efficiently used in the supply-chain context, especially in cases when only two seasonal cycles of data are available. We propose a new order selection algorithm for the model and compare its performance with the conventional ARIMA on real data. We show that the proposed model performs well in terms of both accuracy and computational time in comparison with other ARIMA implementations, which makes it efficient in the supply-chain context. 相似文献
2.
Xiaopo Wu Yangming Shi Weibo Meng Xiaofei Ma Nian Fang 《International Journal of Satellite Communications and Networking》2019,37(3):283-291
Electromagnetic signal emitted by satellite communication (satcom) transmitters are used to identify specific individual uplink satcom terminals sharing the common transponder in real environment, which is known as specific emitter identification (SEI) that allows for early indications and warning (I&W) of the targets carrying satcom furnishment and furthermore the real time electromagnetic situation awareness in military operations. In this paper, the authors are the first to propose the identification of specific transmitters of satcom by using probabilistic neural networks (PNN) to reach the goal of target recognition. We have been devoted to the examination by exploring the feasibility of utilizing the Hilbert transform to signal preprocessing, applying the discrete wavelet transform to feature extraction, and employing the PNN to perform the classification of stationary signals. There are a total of 1000 sampling time series with binary phase shift keying (BPSK) modulation originated by five types of satcom transmitters in the test. The established PNNs classifier implements the data testing and finally yields satisfactory accuracy at 8 dB(±1 dB) carrier to noise ratio, which indicates the feasibility of our method, and even the keen insight of its application in military. 相似文献
3.
The integration of reinforcement learning (RL) and imitation learning (IL) is an important problem that has long been studied in the field of intelligent robotics. RL optimizes policies to maximize the cumulative reward, whereas IL attempts to extract general knowledge about the trajectories demonstrated by experts, i.e, demonstrators. Because each has its own drawbacks, many methods combining them and compensating for each set of drawbacks have been explored thus far. However, many of these methods are heuristic and do not have a solid theoretical basis. This paper presents a new theory for integrating RL and IL by extending the probabilistic graphical model (PGM) framework for RL, control as inference. We develop a new PGM for RL with multiple types of rewards, called probabilistic graphical model for Markov decision processes with multiple optimality emissions (pMDP-MO). Furthermore, we demonstrate that the integrated learning method of RL and IL can be formulated as a probabilistic inference of policies on pMDP-MO by considering the discriminator in generative adversarial imitation learning (GAIL) as an additional optimality emission. We adapt the GAIL and task-achievement reward to our proposed framework, achieving significantly better performance than policies trained with baseline methods. 相似文献
4.
The metric representing the wind energy forecast error, when reported as a percent, is calculated quite differently than the error metrics for electricity transmission, electricity load, or in other industries such as manufacturing when they are also reported as a percent. The resulting calculated metric is quite different from what would be reported if the method utilized elsewhere was employed. This paper examines the possible forecast assessment and operational challenges associated with this finding. Concerning the prospects for improvement, the errors reported in MW of energy have a systematic component. With this insight, we developed a model to improve accuracy. 相似文献
5.
利用风电场历史功率数据预测未来一段时间内的风功率,对保障电网安全稳定运行具有重要的意义。本文提出一种基于奇异谱分析SSA(singular spectrum analysis)和长短时记忆LSTM(long-short term memory net⁃work)网络的时序特征预测框架用于短期风功率的预测。首先通过SSA对历史风功率原始数据进行降噪处理,然后经过数据转换之后,以LSTM网络为基础进行预测模型的训练,最后通过某风电场提供的两个风机的历史功率数据进行验证。实验结果表明,奇异谱分析对风电场的历史数据具有良好的降噪性,SSA+LSTM模型在测试数据上取得了较好的预测性能,能够有效进行短期风功率的预测。 相似文献
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7.
目前需求预测在整个印制电路板产业的生产活动控制中正扮演着越来越重要的角色。分析了影响印制电路板需求的因素和现有的预测方法,提出了一种适用于PCB产业需求预测的有效方法——遗传/BP—神经网络。实验表明该方法能够进一步改善印制电路板预测的准确度和减少生产成本的消耗。 相似文献
8.
Logistic模型的改进及在油藏可采储量预测中的应用 总被引:6,自引:2,他引:4
为了更准确地预测油气藏未来的经济可采储量,依据油气藏随开发必然要经历产量递减和生产收支平衡的实际。利用Logistic模型中技术可采储量、产量与时间的相互关系,经推导和改进后,再结合当前经济技术条件下的废弃产油量,预测油气藏的有效生产时间及其经济可呆储量。实践证实,改进后的Logistic模型预测结果更符合油气藏的生产实际,能为油气藏的储量管理提供可靠的依据。 相似文献
9.
条件概率关系数据库模型 总被引:1,自引:0,他引:1
现实世界中大量存在着的不确定性信息,关系数据库模型仅视它们为空值,有必要增强其处理这类信息的能力,文章在总结前人工作的基础上推广关系数据库模型,创建有效处理随机型不确定性信息的条件概念关系数据库模型,该模型通过在关系模式中增加一个条件概率测度属性,为每条记录指定适当的条件概率的途径,来表示不确定性信息。文中以对象码为基本工具,创建了条件概率关系结构;以特征函数为基本工具,定义了一套基于该结构的代数运算规则。条件概率的语意比概率的语意广泛,灵活,因而该模型能有效克服概率关系模型的许多不足。 相似文献
10.