Abstract
In recent years, most of the communication places are using business English manual simultaneous interpretation or electronic equipment translation. In the context of diverse cultures, the way English is used and its grammar vary from country to country. In the face of this situation, how to optimize business English translation technology and improve the accuracy of business communication content is one of the research contents of scholars all over the world. This paper first introduces the purpose of business English translation and the gap between business English translation and general English translation. Secondly, a genetic algorithm is used to optimize the structure of the BP neural network, and the combination of the two improves the ability of translation search. This paper compares the influence of the traditional BP algorithm and the BP algorithm optimized by genetic algorithm on the construction of a business English translation model. The results show that BP neural network optimized by the genetic algorithm can improve the speed of business English text translation, reduce the impact of semantic errors on the accuracy of the translation model, and improve the efficiency of translation.
Highlights
With the development of globalization in various fields, business English has become more widely used
In order to solve the problem of complex model structure in the process of translation training, this paper proposes a structure optimization method of BP neural network based on a genetic algorithm
In order to solve the problem of complex training caused by the multilayer structure of neural network algorithm in the training process, we propose the method of using a genetic algorithm to optimize the structure of the neural network [18]
Summary
With the development of globalization in various fields, business English has become more widely used. E regression model based on the time series and variable data of the LSTM neural network algorithm can predict the effect and time accuracy of business English translation [14]. The learning effect of the model is analyzed from the experimental results and operation process of the constructed neural network algorithm and particle swarm optimization algorithm model, which helps to improve the overall performance and work level of business English translation. In the process of optimizing BP neural network structure by genetic algorithm, the model of solving nonlinear variables is constructed according to the rules of the GA algorithm. 3. Analysis of Business English Translation Model Based on BP Neural Network Optimized by Genetic Algorithm.
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