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Web of Proceedings - Francis Academic Press
Web of Proceedings - Francis Academic Press

English Translation Ability Assessment Based on Neural Network Algorithm of Particle Swarm Optimization

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DOI: 10.25236/ichess.2023.013


Mu Yuan

Corresponding Author

Mu Yuan


The combination of particle swarm optimization algorithm and neural network, which has emerged in current years, can enhance the capability of global search for optimality, and enhance the speed of convergence. The unity of particle swarm algorithm and neural network is suitable for English teaching. The trained particle swarm optimization neural network pattern is utilized to analyze the correctness of student' English translation ability by learning and preparing the extracted students' translation samples, which helps teachers to estimate the students' translation capability level and offer a reference for the following teaching. The model is based the math pattern of the particle swarm optimization algorithm and the foundational principles of the artificial neural network framework, and the study capability analysis pattern is proposed to identify the topology of the neural network and the number of nodes in the hidden layer of the model. The results of the case application show that the research framework could improve English translation teaching ability and teaching and studying.


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