Dual-optimized adaptive Kalman filtering algorithm based on BP neural network and variance compensation for laser absorption spectroscopy

The current music teaching can effectively improve students’ music emotional expression indirectly. How to use the PSO-BP neural network to realize the quantitative research of music emotional expression is the current development trend. Based on this, this paper studies the influence factors of music emotion expression based on PSO-BP neural network and big data analysis. Firstly, a music emotion expression analysis model based on PSO-BP neural network algorithm is proposed. The autocorrelation function is used to simulate the emotion expression information in music. Through the maximum value of the autocorrelation function curve in the detection process, the vocal music signal is restored, and then the emotion expressed is analyzed. Secondly, the influence factors of PSO-BP neural network algorithm in music emotion expression are analyzed. The improved PSO-BP neural network algorithm and multidimensional data model are used for comprehensive analysis to accurately analyze the emotion in music expression, and the fuzzy evaluation method and analytic hierarchy process are used for quality evaluation. Finally, the validity of the music emotion analysis model is verified by many experiments.

Section: Introductionmentioning

confidence: 99%

[Retracted] Music Emotion Analysis Based on PSO‐BP Neural Network and Big Data Analysis

Chen

2021

“…And most of them did not involve intelligent algorithms combined with distributed computing technology and did not apply intelligent solutions to the study of prestress loss of bridges [18]. On the other hand, although a lot of basic research has been done on the design and mechanical performance analysis of the bridge prestress under the fatigue load, the research results on the mechanical performance analysis and bearing capacity design of the bridge under different types of fatigue load are relatively few, and there is no strong robust model for limit load effect analysis [19]. Based on this background, this paper studies the method of determining the loss of prestress and modeling the mechanical properties of the bridge under fatigue load based on PSO-BP neural network algorithm.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Determination of Bridge Prestress Loss under Fatigue Load Based on PSO‐BP Neural Network

Wang

2021

During the service period of a prestressed concrete bridge, as the number of cyclic loads increases, cumulative fatigue damage and prestress loss will occur inside the structure, which will affect the safety, durability, and service life of the structure. Based on this, this paper studies the loss of bridge prestress under fatigue load. First, the relationship between the prestress loss of the prestressed tendons and the residual deflection of the test beam is analyzed. Based on the test results and the main influencing factors of fatigue and creep, a concrete fatigue and creep calculation model is proposed; then, based on the static cracking check calculation method and POS-BP neural network algorithm, a prestressed concrete beam fatigue cracking check model under repeated loads is proposed. Finally, the mechanical performance of the prestressed concrete beam after fatigue loading is analyzed, and the influence of the fatigue load on the bearing capacity of the prestressed concrete beam is explored. The results show that the bridge prestress loss characterization model based on the POS-BP neural network algorithm has the advantages of high calculation efficiency and strong applicability.

“…On the other hand, the research has not focused on the wrestler action recognition and related model construction based on the GB-BP neural network algorithm [ 19 ]. Therefore, from the existing research results and wrestler recognition patterns, it is difficult to achieve the effect of intelligent recognition and classification [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Sports Action Recognition Based on GB‐BP Neural Network and Big Data Analysis

Wang

Qiu

2021

In recent years, the application of the gradient boosting-back propagation (GB-BP) neural network algorithm in many industries has brought huge benefits, so how to combine the GB-BP neural network algorithm with sports has become a research hotspot. Based on this, this paper studies the application of the GB-BP neural network algorithm in wrestling, designs the sports athletes action recognition and classification model based on the GB-BP neural network algorithm, first analyzes the research status of wrestling action recognition, and then optimizes and improves the shortcomings of action recognition and big data analysis technology. The GB-BP neural network algorithm can realize the accurate recognition and classification of wrestlers’ training actions and carry out big data mining analysis with known action recognition, so as to achieve accurate classification. The experimental results show that the model can play a good role in wrestling and effectively improve the efficiency of wrestlers in training.