Furthermore, introduced a new forecast model based on BP neural network, a real diagnosis is done by the investigated data, the results show that the bullwhip effect and their variable are different due to different forecasting techniques, it is the BP neural network that makes the best method...
This paper aims to compare the performance of different Artificial Neural Networks techniques for tourist demand forecasting. We test the forecasting accuracy of three different types of architectures: a multi-layer perceptron, a radial basis function and an Elman network. We also evaluate the effect...
2016). To overcome the issue, numerous studies have exploited machine and deep learning techniques in OD demand prediction, such as convolutional neural networks (Ke et al. 2017), stacked gradient boosting decision trees (W. Wu, Xia, and Jin 2021), and long short-term memory (Baghbani, Bo...
To overcome these challenges, textile manufacturers can use a range of tools and techniques, including automated scheduling software, predictive analytics, and real-time monitoring systems. Printing and Packaging: The printing and packaging industry also uses batch production scheduling. This is because ...
The proposed work includes both point forecasting and interval forecasting techniques in wind speed prediction. The point prediction errors are characterised using suitable statistical distribution and quantified by means of confidence intervals and coverage rate. The work, in the first stage, forecasts th...
Particle size significantly influences handling, storage, granulation techniques, and agronomic responses. To accurately measure the particle sizes, 30 random fertilizer samples were selected, each corresponding to a different type of composition according to Tables 4 and 5, as shown in Fig. 2a and ...
Land use demand forecasting using the SD model The SD model proposes that complex systems are made up of many information feedback mechanisms between subsystems that may be simulated on computers. Based on earlier research63,64, this study splits Wuhan’s land use demand system into four subsy...
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Here, we apply efficient, gradient-based optimization techniques with full knowledge of the analytical gradients. The workflow's main idea is to translate the different process models into generic CasADi optimization syntax. Compared to data-driven MPC approaches in scientific literature, which often ...
Examples for general forecasting techniques are the exponential smoothing method (McNeil et al., 2015) or the SARIMA model. A more advanced approach is to use the wavelet transform as e.g. discussed by Schlüter and Deuschle (2014), who generate point forecasts by splitting up a time series ...