BP Neural Network Optimization Considering Computational Resource Constraints

BP neural networks have a wide range of applications and can cope with a variety of problems. In the past, many scholars have applied neural networks to various problems. However, BP neural networks require a large number of resources for computation during training, which leads to problems such as...

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Bibliographic Details
Published in2024 IEEE 2nd International Conference on Image Processing and Computer Applications (ICIPCA) pp. 1452 - 1457
Main Authors Luo, Weihan, Li, Zhiyi, Peng, Fangwei
Format Conference Proceeding
LanguageEnglish
Published IEEE 28.06.2024
Subjects
BP neural network
Forward propagation
Genetic algorithm
Genetic algorithms
Image processing
Inference algorithms
Limiting
Mobile handsets
Neural networks
Optimization
Performance evaluation
Prediction algorithms
Resource constrained
Training
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DOI10.1109/ICIPCA61593.2024.10709056

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Summary:BP neural networks have a wide range of applications and can cope with a variety of problems. In the past, many scholars have applied neural networks to various problems. However, BP neural networks require a large number of resources for computation during training, which leads to problems such as long computation time when the problem is more complex. In order to solve this problem, this paper limits the number of running iterations of the neural network by considering the condition of limited computational resources. To enhance prediction accuracy, when employing the genetic algorithm for optimizing the BP neural network, the forward propagation process of the neural network is utilized to compute the fitness of each iteration. This approach serves to enhance the optimization efficiency of the algorithm. Experiments show that the IGABP algorithm designed in this paper has a better prediction performance in all three indexes: MSE, MAE and MAPE.
DOI:10.1109/ICIPCA61593.2024.10709056