scholarly journals Martial Arts Competitive Decision-Making Algorithm Based on Improved BP Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Huipeng Lv
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Bp Neural Network ◽  
Martial Arts ◽  
Gradient Descent ◽  
Step Length ◽  
Learning Rate ◽  
Main Body ◽  
Scientific Analysis ◽  
Neural Network Technology

The main body of modern Chinese martial arts competition is the strategy, and fighting has just started in sports competitions. Strategy and action correspond to each other and practice as a set. Therefore, constructing the Chinese martial arts competition decision-making algorithm and perfecting the martial arts competition are intuitive and essential. The formulation of martial arts competition strategies requires scientific analysis of athletic data and more accurate predictions. Based on this observation, this paper combines the popular neural network technology to propose a novel additional momentum-elastic gradient descent. The BP neural network adapts to the learning rate. The algorithm is improved for the traditional BP neural network, such as selecting learning step length, the difficulty of determining the size, and direction of the weight, and the learning rate is not easy to control. The experimental results show that this paper’s algorithm has improved both network scale and running time and can predict martial arts competition routines and formulate scientific strategies.

Applied Research Of BP Neural Network In Earthquake Prediction

2012 ◽  
Vol 204-208 ◽  
pp. 2449-2454 ◽  
Author(s):  
Wu Sheng Hu ◽  
Hong Lin Nie ◽  
Hao Wang
Keyword(s):  
Neural Network ◽  
Earthquake Prediction ◽  
Bp Neural Network ◽  
Integrated Model ◽  
Earthquake Magnitude ◽  
Linear Regression Method ◽  
Network Technology ◽  
Short Term ◽  
Non Linear ◽  
Neural Network Technology

Nowadays, earthquake prediction is still a worldwide scientific problem, especially the prediction for short-term and imminent earthquake has no substantial breakthroughs. BP neural network technology has a strong non-linear mapping function which could better reflect the strong non-linear relationship between earthquake precursors and the time and the magnitude of a potential earthquake. In this paper, we selected the region of Beijing as the research area and 3 months as the prediction period. Based on BP neural network and integrated with the conventional linear regression method, a regional short-term integrated model was established, which gives the quantitative prediction for the earthquake magnitude. The results show that the earthquake magnitude prediction RMSE (root mean square error) of the integrated model reaches ± 0.28 Ms. Compared with conventional methods, the integrated model improves significantly. The new model has a good prospect to use BP neural network technology for earthquake prediction.

scholarly journals Cognitive Electronic Jamming Decision-Making Method Based on Improved Q -Learning Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Huiqin Li ◽  
Yanling Li ◽  
Chuan He ◽  
Jianwei Zhan ◽  
Hui Zhang
Keyword(s):  
Decision Making ◽  
Gradient Descent ◽  
Learning Algorithm ◽  
Learning Rate ◽  
Stochastic Gradient Descent ◽  
Local Optima ◽  
Q Learning ◽  
Exploration Strategy ◽  
Decision Making Model ◽  
Metropolis Criterion

In this paper, a cognitive electronic jamming decision-making method based on improved Q -learning is proposed to improve the efficiency of radar jamming decision-making. First, the method adopts the simulated annealing (SA) algorithm’s Metropolis criterion to enhance the exploration strategy, balancing the contradictory relationship between exploration and utilization in the algorithm to avoid falling into local optima. At the same time, the idea of stochastic gradient descent with warm restarts (SGDR) is introduced to improve the learning rate of the algorithm, which reduces the oscillation and improves convergence speed at the later stage of the algorithm iteration. Then, a cognitive electronic jamming decision-making model is constructed, and the improved Q -learning algorithm’s specific steps are given. The simulation experiment takes a multifunctional radar as an example to analyze the influence of exploration strategy and learning rate on decision-making performance. The results reveal that compared with the traditional Q -learning algorithm, the improved Q -learning algorithm proposed in this paper can fully explore and efficiently utilize and converge the results to a better solution at a faster speed. The number of iterations can be reduced to more than 50%, which proves the feasibility and effectiveness of the method applied to cognitive electronic jamming decision-making.

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