Two Types of Neural Network Prediction Models and Time Complexity
| Autor: | Subhash Rekheja, Ji Min Zhang, Liang Zhu |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Quantitative Biology::Neurons and Cognition
Artificial neural network Time delay neural network business.industry Computer science Deep learning Computer Science::Neural and Evolutionary Computation General Medicine Probabilistic neural network Recurrent neural network Feedforward neural network Artificial intelligence Types of artificial neural networks business Stochastic neural network Algorithm |
| Zdroj: | Applied Mechanics and Materials. :985-989 |
| ISSN: | 1662-7482 |
| DOI: | 10.4028/www.scientific.net/amm.548-549.985 |
| Popis: | The linear adaptive neural network and RBF neural network, according to the measured low-pass filter lateral acceleration signal, was used to establish the reference lateral acceleration applied for the input of tilting train control system. This paper presents the two types of neural network models and prediction algorithms, and studies the time complexity of the two types of network algorithms. The results show that time complexity of the neural network prediction is closely related to its parameters, the neural network structure also can lead to the difference in their calculation time, and RBF prediction neural network spends the minimum time. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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