基于小龙虾优化算法的深度置信网络在听力效果自动评估方法中的设计与应用

Ying Cheng
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引用次数: 0

摘要

引言:听力策略分析与评估不仅需要客观公正的听力策略分析,还需要高精度、高实时性的评估模型,更需要对听力评估的影响因素进行深入分析和特征提取:方法:本文提出了一种基于小龙虾优化算法的深度置信网络自动评测方法。首先,通过分析听力改进策略,构建多维听力策略评估体系;然后,通过小龙虾优化算法改进深度置信网络,构建自动评估模型;最后,通过仿真实验分析:结果:所提出的方法提高了评估的准确性、鲁棒性和实时性。该方法基于小龙虾优化算法的深度置信网络,自动评测值相对误差的绝对值控制在0.011范围内,评测时间小于0.005 s。结论:解决了自动评测方法应用不具体、普适性差、评测精度低、实时性差等问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Design and Use of Deep Confidence Network Based on Crayfish Optimization Algorithm in Automatic Assessment Method of Hearing Effectiveness
INTRODCTION: Listening strategy analysis and assessment not only need objective and fair sound listening strategy analysis, but also need high-precision and high real-time assessment model, and even more need in-depth analysis and feature extraction of the influencing factors of listening assessment.OBJECTIVES: To address the problems of current automatic assessment methods, such as non-specific application, poor generalization, low assessment accuracy, and poor real-time performance.METHODS: This paper proposes an automatic assessment method based on a deep confidence network based on crawfish optimization algorithm. First, the multi-dimensional listening strategy evaluation system is constructed by analyzing the listening improvement strategy; then, the depth confidence network is improved by the crayfish optimization algorithm to construct the automatic evaluation model; finally, through the analysis of simulation experiments.RESLUTS: The proposed method improves the evaluation accuracy, robustness, and real-time performance. The absolute value of the relative error of the automatic evaluation value of the proposed method is controlled in the range of 0.011, and the evaluation time is less than 0.005 s. The method is based on a deep confidence network based on the crayfish optimization algorithm.CONCLUSION: The problems of non-specific application of automated assessment methods, poor generalization, low assessment accuracy, and poor real-time performance are addressed. 
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