Research on the Method of Warfare Simulation Data Analysis Based on BP Neural Network and RoughSet

2013 ◽  
Author(s):  
Heng Jun ◽  
Dong Yan-Hong ◽  
Wang Jin-Shu ◽  
Yan Peng
Keyword(s):  
Neural Network ◽  
Data Analysis ◽  
Bp Neural Network ◽  
Simulation Data

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.

scholarly journals Neural Network: An Improved FCM for Multimodal Cultural Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yan Wang ◽  
Di Liu ◽  
Lingling Tian ◽  
Aiping Tan
Keyword(s):  
Neural Network ◽  
Data Analysis ◽  
Bp Neural Network ◽  
Clustering Algorithm ◽  
Cultural Analysis ◽  
Fuzzy C Means ◽  
Correlation Degree ◽  
Feature Information ◽  
Cultural Data Analysis ◽  
Cultural Data

With the development of cloud computing, big data, and artificial intelligence (AI) technology, there is a growing interest in “cultural analysis.” Cultural analysis requires different types of data such as texts, pictures, and videos. The richness and differences of resources in the cultural field lead to diverse modalities of cultural data. Traditional text analysis methods can no longer meet the data analysis needs of current multimedia cultural resources. This article starts from cultural data’s feature information to solve the heterogeneity problem faced by massive multimodal cultural data analysis. It analyzes it from geography, time, art, and thematic character, classified and aggregated to form a multimodal cultural feature information matrix. The corresponding correlation measurement methods for different matrices from the above dimensions are proposed, solved in turn, and substituted into the optimized training back propagation (BP) neural network to obtain the final correlation degree. The improved fuzzy C-means (FCM) clustering algorithm is used to aggregate the high correlation cultural data based on the degree. The algorithm proposed in this study is compared with the existing algorithm. The experimental results show that the optimized BP neural network is at least 58% more accurate than the current method for calculating different matrices’ correlation degrees. In terms of accuracy, the improved fuzzy C-means algorithm effectively reduces the random interference in the selection of the initial clustering center, which is significantly higher than other clustering algorithms.

Research on the Method of Warfare Simulation Data Analysis Based on BP Neural Network and RoughSet

2013 ◽  
Vol 765-767 ◽  
pp. 498-503
Author(s):  
Jun Heng ◽  
Yun Fei Tian ◽  
Peng Yan
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Rough Set ◽  
Bp Neural Network ◽  
Rough Set Theory ◽  
Reference Value ◽  
Simulation Data ◽  
Bp Network ◽  
The Neural Network ◽  
Rule Set

Preliminary exploration on simulation data mining with BP network and rough set method is made in the paper. Data mining method based on rough set theory and BP neural network is put up. Firstly, we reduce the redundant attributes in the decision-making table with rough set method, and then the noise is filtered by BP neural network. Finally, rule set is generated by rough set from the reduction decision table. This method not only avoids the complexity of the rules extracted from training the neural network, but also improves the classification accuracy effectively. At last, the mining data by the experimental data of the troops marching warfare simulation system and the result with the actual reference value is obtained.

Application of a genetic algorithm to optimize BP neural networks

2020 ◽  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Local Minimum ◽  
Network Performance ◽  
Learning Algorithm ◽  
Simulation Data ◽  
Neural Network Learning ◽  
Network Learning ◽  
Fast Convergence Rate

A genetic algorithm is proposed to us to prevent a local minimum defect when using the BP neural network learning algorithm. The genetic algorithm is first used to optimize the weight and threshold of the BP neural network, and then obtained values are used to optimize the BP neural network. Optimized network performance is estimated using simulation data. The results of numerical simulations show that the BP neural network optimized by the genetic algorithm can effectively eliminate a local minimum defect, which is easy to find in the original BP neural network, and has the advantages of fast convergence rate and high accuracy. Keywords BP neural network; genetic algorithm; local minimum defect; optimization

Sign in / Sign up

Export Citation Format

Share Document