research-article

Audience Evaluation and Analysis of Symphony Performance Effects Based on the Genetic Neural Network Algorithm for the Multilayer Perceptron (GA-MLP-NN)

Author:

Cong Yan Academic Editor:

Syed Hassan AhmedAuthors Info & Claims

Computational Intelligence and Neuroscience, Volume 2021

https://doi.org/10.1155/2021/4133892

Published: 01 January 2021 Publication History

Abstract

Traditional symphony performances need to obtain a large amount of data in terms of effect evaluation to ensure the authenticity and stability of the data. In the process of processing the audience evaluation data, there are problems such as large calculation dimensions and low data relevance. Based on this, this article studies the audience evaluation model of teaching quality based on the multilayer perceptron genetic neural network algorithm for the data processing link in the evaluation of the symphony performance effect. Multilayer perceptrons are combined to collect data on the audience’s evaluation information; genetic neural network algorithm is used for comprehensive analysis to realize multivariate analysis and objective evaluation of all vocal data of the symphony performance process and effects according to different characteristics and expressions of the audience evaluation. Changes are analyzed and evaluated accurately. The experimental results show that the performance evaluation model of symphony performance based on the multilayer perceptron genetic neural network algorithm can be quantitatively evaluated in real time and is at least higher in accuracy than the results obtained by the mainstream evaluation method of data postprocessing with optimized iterative algorithms as the core 23.1%, its scope of application is also wider, and it has important practical significance in real-time quantitative evaluation of the effect of symphony performance.

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Cited By

Intelligence and Neuroscience C(2023)RetractedComputational Intelligence and Neuroscience10.1155/2023/98417122023Online publication date: 1-Jan-2023
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Published In

cover image Computational Intelligence and Neuroscience

Computational Intelligence and Neuroscience Volume 2021, Issue

2021

8452 pages

ISSN:1687-5265

EISSN:1687-5273

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Copyright © 2021 Cong Yan.

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Hindawi Limited

London, United Kingdom

Publication History

Published: 01 January 2021

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Cited By

Intelligence and Neuroscience C(2023)RetractedComputational Intelligence and Neuroscience10.1155/2023/98417122023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/9841712
Yang PYan SZhu DWang JWu FYan ZYan S(2022)Improved Sparrow Algorithm Based on Game Predatory Mechanism and Suicide MechanismComputational Intelligence and Neuroscience10.1155/2022/49254162022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/4925416
Wu XWei DVasgi BOleiwi ABangare SAsenso E(2022)Research on Network Security Situational Awareness Based on Crawler AlgorithmSecurity and Communication Networks10.1155/2022/36391742022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/3639174

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