Steel Prices Index Prediction in China Based on BP Neural Network

Liu, Zhishuo; Wang, Yongcong; Zhu, Shuang; Zhang, Baopeng; Wei, Lingyun

doi:10.1007/978-3-662-43871-8_87

Cited by 16 publications

(6 citation statements)

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“…Therefore, this study applied a DNN to train the samples and learn the optimal state action relationships from the generated data. A DNN architecture [29] was designed with different layers and neurons per layer. Specifically, it comprised an input layer, an output layer, and three fully connected hidden layers, with each layer consisting of 20 neurons.…”

Section: Database Trainingmentioning

confidence: 99%

Minimum-Data-Driven Guidance for Impact Angle Control

Liu,

Wang,

et al. 2024

Aerospace

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This paper investigates the impact-angle-control guidance problem for varying-speed flight vehicles with constrained acceleration. A learning-based bias proportional navigation guidance (L-BPN) law is proposed to achieve impact-angle-constrained impact by constructing a deep neural network (DNN) for nonlinear mapping between the impact angle and the bias term. During the process of dataset establishment, the impact of state variables is evaluated by sensitivity analysis to minimize the quantity of training data. This approach also effectively accelerates sample generation and improves the training efficiency. The simulation results verify the effectiveness of the proposed L-BPN law and demonstrate its advantages over the existing algorithms.

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Section: Database Trainingmentioning

confidence: 99%

Minimum-Data-Driven Guidance for Impact Angle Control

Liu,

Wang,

et al. 2024

Aerospace

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“…Ganokratanaa and Ketcham [211] investigate the deep learning neuron network for steel price index forecasting and propose the use of this approach as part of steel purchasing decision making. Similarly, Liu, Wang, Zhu, Zhang, and Wei [212] find the potential usefulness of the back propagation neural network for steel price index forecasts. Benrhmach, Namir, Namir, and Bouyaghroumni [213] examine the application of the non-linear auto-regressive neural network with the extended Kalman filter algorithm for steel price forecasting and demonstrate that the approach leads to good forecast accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

Scrap steel price forecasting with neural networks for east, north, south, central, northeast, and southwest China and at the national level

Zhang

2023

Ironmaking & Steelmaking

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There is little doubt about importance of forecasting commodity prices to policy makers and diverse varieties of market participants. In this present work, we analyse price forecasting problems for scrap steel for east, north, south, central, northeast, and southwest China and at the national level with daily data during 08/23/2013-04/15/2021. We focus on exploration of usefulness of non-linear autoregressive neural networks and examine forecasting performance based upon a variety of settings over algorithms for model estimations, numbers of hidden neurons and delays, and ratios for splitting the data into training, validation, and testing phases. We arrive at relatively simple models for the seven price series, which lead to forecasts of high accuracy and stabilities with relative root mean square errors below 0.85%. Our results could serve as standalone technical forecasts. They could be combined with other forecasts for forming perspectives of price trends and carrying out policy analysis.

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“…The research results show that the closer the data is, the more accurate the prediction results will be, and the short-term prediction effect will be better than the long-term prediction effect. Li Yinglu passes through BPNN [10].…”

Section: Domestic Research On Digital Currency Price Predictionmentioning

confidence: 99%

Research on Price Prediction of Digital Currency Based on Machine Learning

Liu

2023

AEMPS

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Since the invention of digital currency, the social recognition and demand for special currency and similar cryptocurrencies have increased significantly with the development of digital currency and blockchain technology. The rapid rise in the price of digital currency and its significant volatility attracts a large number of users to invest in it as a digital asset. Before the formation of a regulatory strategy with a standardized system, the development of digital currency will undoubtedly have an increasing impact on society, and its price fluctuation will become an unstable factor in society by increasing the risk to users. Therefore, finding out the factors that affect the price of digital currency and forecasting its price has become the focus of research on Bitcoin in recent years. This will not only help investors and relevant institutions understand Bitcoin and the digital currency market but also help improve the financial market and its policies. In this paper, I use a machine learning model to predict the price of digital currency from both numerical and trend aspects. The main contents include the following elements.First, in terms of data characteristics, I comprehensively summarized and referred to the previous research on the price of special currency at home and abroad. Second, in terms of model prediction, I designed a two-stage feature processing method, used three kinds of recurrent neural network models to compare and predict the price of digital currency, and used recursive feature elimination (RFE) and logical regression (LR), random forest (RF), linear discriminant analysis (LDA) Support Vector Machine (SVM) and Naive Bayes (NB), five commonly used machine learning models, are combined to predict the price trend of Bitcoin.

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Steel Prices Index Prediction in China Based on BP Neural Network

Cited by 16 publications

References 0 publications

Minimum-Data-Driven Guidance for Impact Angle Control

Minimum-Data-Driven Guidance for Impact Angle Control

Scrap steel price forecasting with neural networks for east, north, south, central, northeast, and southwest China and at the national level

Research on Price Prediction of Digital Currency Based on Machine Learning

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