Fuzzy Adaptive PSO-ELM Algorithm Applied to Vehicle Sound Quality Prediction

When dealing with specific tasks, the hidden layer output matrix of an extreme learning machine (ELM) may change, largely due to the random assigned weight matrix of the input layer and the threshold matrix of the hidden layer, which sequentially leads to the corresponding change to output weights....

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Bibliographic Details
Published inApplied sciences Vol. 13; no. 17; p. 9561
Main Authors Wang, Chenlin, Yang, Gongzhuo, Li, Junyu, Huang, Qibai
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.09.2023
Subjects
Accuracy
Acoustics
Algorithms
Back propagation
extreme learning machine
fuzzy control
Genetic algorithms
Information sharing
Mathematical optimization
Methods
Neural networks
Optimization algorithms
particle swarm optimization
Propagation
Saturn
sound quality
Support vector machines
Velocity
Wavelet transforms
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Summary:When dealing with specific tasks, the hidden layer output matrix of an extreme learning machine (ELM) may change, largely due to the random assigned weight matrix of the input layer and the threshold matrix of the hidden layer, which sequentially leads to the corresponding change to output weights. The unstable fluctuations of the output weights increase the structural risk and the empirical risk of ELM. This paper proposed a fuzzy adaptive particle swarm optimization (PSO) algorithm to solve this problem, which could nonlinearly control the inertia factor during the iteration by fuzzy control. Based on the fuzzy adaptive PSO-ELM algorithm, a sound quality prediction model was developed. The prediction results of this model were compared with the other three sound quality prediction models. The results showed that the fuzzy adaptive PSO-ELM model was more precise. In addition, in comparison with two other adaptive inertia factor algorithms, the fuzzy adaptive PSO-ELM model was the fastest model to reach goal accuracy.
ISSN:2076-3417
2076-3417
DOI:10.3390/app13179561