scholarly journals Music Emotion Analysis Based on PSO-BP Neural Network and Big Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Chen Xi
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Emotional Expression ◽  
Bp Neural Network ◽  
Influence Factors ◽  
Big Data Analysis ◽  
Emotion Expression ◽  
Analysis Model ◽  
Network Algorithm ◽  
Neural Network Algorithm

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.

scholarly journals Sports Action Recognition Based on GB-BP Neural Network and Big Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Lidong Wang ◽  
Kai Qiu ◽  
Wang Li
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Data Analysis ◽  
Action Recognition ◽  
Bp Neural Network ◽  
Big Data Analysis ◽  
Classification Model ◽  
Gradient Boosting ◽  
Network Algorithm ◽  
Neural Network Algorithm

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.

Simulation Analysis of Collaborative Efficiency Improvement of Innovative Talents Management in Colleges and Universities Based on BP Neural Network

2021 ◽  
Vol 7 (5) ◽  
pp. 4449-4462
Author(s):  
Xiyin Chang ◽  
Yuchun Sun
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Simulation Analysis ◽  
Talent Management ◽  
Colleges And Universities ◽  
Management Model ◽  
Analysis Model ◽  
Calculation Time ◽  
Network Algorithm ◽  
Neural Network Algorithm

Objectives: In recent years, it is more and more difficult to manage innovative talents. In order to improve the collaborative efficiency of innovative talents management, this paper presents a simulation analysis of collaborative efficiency of innovative talents management in Colleges and Universities Based on BP neural network algorithm. Methods: Data simulation technology is used to establish talent management model. This model puts forward the optimization scheme from the algorithm flow, and improves the synergy of talent management by using data transformation technology. This model is analyzed from two aspects of universities and talents. BP neural network algorithm is added to the calculation of management efficiency to realize the sequence optimization of data. Results: In order to test the authenticity and efficiency of the algorithm in the talent management model, a comparative experiment is set up to analyze the results. The test results show that the accuracy of the optimized data analysis model is generally above 95%, while the accuracy of the traditional algorithm is generally below 80%, the collaborative efficiency calculation time of talent management model is the shortest, averaging only about 15 seconds; the traditional model calculation time is very unstable, from short 12 seconds to long 45 seconds, the calculation span is very large, and the accuracy rate is low. Conclusion: The research shows that BP neural network algorithm can improve the synergy of management and optimize the management mode of innovative talents, which is worthy of further promotion.

Precision Power System Harmonic Analysis Algorithm Based on ABC-BPNN

2013 ◽  
Vol 483 ◽  
pp. 630-634
Author(s):  
Shu Chuan Gan ◽  
Ling Tang ◽  
Li Cao ◽  
Ying Gao Yue
Keyword(s):  
Neural Network ◽  
Power System ◽  
Harmonic Analysis ◽  
Bp Neural Network ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimal Solution ◽  
Bee Colony ◽  
Network Algorithm ◽  
Neural Network Algorithm

An algorithm of artificial colony algorithm to optimize the BP neural network algorithm was presented and used to analyze the harmonics of power system. The artificial bee colony algorithm global searching ability, convergence speed for the BP neural network algorithm for harmonic analysis is easy to fall into local optimal solution of the disadvantages, and the initial weights of the artificial bee colony algorithm also greatly enhance whole algorithm model generalization capability. This algorithm using MATLAB for Artificial bee colony algorithm and BP neural network algorithm simulation training toolbox found using artificial bee colony algorithm to optimize BP neural network algorithm converges faster results with greater accuracy, with better harmonic analysis results.

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