At present, General Regression Neural Network (GRNN) have been more and more used for data prediction in industry, however, because its smoothing factor is ...
[15] The research is devoted to solving the issues of increasing the efficiency of using general regression neural networks in solving predictive problems. This ...
Key takeaway: 'The Parallel Integrated Neural Network System (PINN) improves prediction accuracy in industrial data prediction compared to other models, ...
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At present, General Regression Neural Network (GRNN) have been more and more used for data prediction in industry, however, because its smoothing factor is ...
The proposed parallel hybrid neural network is evaluated by two public datasets, in detail, an aircraft turbofan engine dataset and a milling dataset.
An effective Parallel Integrated Neural Network System for industrial data prediction ... Hyper-parameter selection in deep neural networks using parallel ...
The aim of the study is to develop a predictive model of performance management of innovative industrial systems by building neural networks. The research ...
Jul 11, 2023 · Abstract—Time-series prediction plays a crucial role in the. Industrial Internet of Things (IIoT) to enable intelligent process.
Oct 23, 2019 · Neural networks ARE machine learning algorithms, but I assume your real question is “When should I use neural networks rather than other ML ...
Hardware implementation of an FIR neural network for applications in times series data prediction, a variation of a standard neural network called as finite ...
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