Comprehensive Review of Backpropagation Neural Networks

Authors

  • Mingfeng Li

DOI:

https://doi.org/10.54097/51y16r47

Keywords:

Backpropagation Neural Network (BPNN); Deep learning; Network structure; Optimization algorithms.

Abstract

The Backpropagation Neural Network (BPNN) is a deep learning model inspired by the biological neural network. Introduced in the 1980s, the BPNN quickly became a focal point in neural network research due to its outstanding learning capability and adaptability. The network structure consists of input, hidden, and output layers, and it optimizes weights through the backpropagation algorithm, widely applied in image recognition, speech processing, natural language processing, and more. The mathematical model of neurons describes the relationship between input and output, and the training process involves adjusting weights and biases using optimization algorithms like gradient descent. In applications, BPNN excels in image recognition, speech processing, natural language processing, and financial forecasting. Researchers continuously experiment with optimization algorithms, including the Grey Wolf Algorithm, Genetic Algorithm, Particle Swarm Algorithm, Simulated Annealing Algorithm, as well as comprehensive strategies and improved gradient descent algorithms. In the future, with the ongoing development of deep learning, BPNN is poised to play a crucial role in tasks such as image recognition and speech processing.

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References

Yang S, Luo L, Tan B. Research on Sports Performance Prediction Based on BP Neural Network[J]. Mobile Information Systems, 2021,2021:1-8.

Bai Y, Luo M, Pang F. An Algorithm for Solving Robot Inverse Kinematics Based on FOA Optimized BP Neural Network[J]. Applied Sciences, 2021,11(15):7129.

Liu Y, Dai J, Zhao S, et al. A bidirectional reflectance distribution function model of space targets in visible spectrum based on GA-BP network[J]. Applied Physics B, 2020,126(6).

Yang J, Hu Y, Zhang K, et al. An improved evolution algorithm using population competition genetic algorithm and self-correction BP neural network based on fitness landscape[J]. Soft Computing, 2021,25(3):1751-1776.

Quan G, Zhang Y, Lei S, et al. Characterization of Flow Behaviors by a PSO-BP Integrated Model for a Medium Carbon Alloy Steel[J]. Materials, 2023,16(8):2982.

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Published

20-01-2024

Issue

Section

Articles

How to Cite

Comprehensive Review of Backpropagation Neural Networks. (2024). Academic Journal of Science and Technology, 9(1), 150-154. https://doi.org/10.54097/51y16r47

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