Solution to China’s GDP Prediction Problem by BP Neural Network

2011 ◽  
Vol 50-51 ◽  
pp. 423-427
Author(s):  
Juan Li ◽  
Ya Feng Ya ◽  
Hai Ming Wu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Learning Rate ◽  
Prediction Problem ◽  
Previous Time

Because the choice and important of learning rate , the higher of η and the faster convergence it will be, but it may cause instability or function vibration if is too high; if is lower, although it may avoid instability, the speed of function convergence will reduce. In order to solve the contradiction, we introduce a variable of , and if the this time is the same as that of the previous time, the weighted summation value will increase and it results in the regulation speed of right value at the stable regulation; and if the this time is contrary to that of the previous time, it indicates that a certain vibration and now the result of summation will make the value of decrease to play a role in stability and increase the speed of function convergence.

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.

Research on Improved BP Learning Algorithm of BP Neural Network

2013 ◽  
Vol 765-767 ◽  
pp. 1644-1647 ◽  
Author(s):  
Jian Li Chu ◽  
Hong Yan Li ◽  
Xiao Ji Chen
Keyword(s):  
Neural Network ◽  
Conjugate Gradient ◽  
Bp Neural Network ◽  
Learning Algorithm ◽  
Error Function ◽  
Learning Rate ◽  
Good Effect ◽  
Dynamic Learning ◽  
Neural Network Learning ◽  
Hidden Layer

Aiming at the existence of the BP neural network learning algorithm in the slow learning speed, the possibility of failure is large, poor generalization ability, there are multiple issues, extreme value point and network structure are difficult to determine, in this paper, we study algorithm improvement methods. Explain the algorithm principle, on the basis of three improved methods are studied, respectively is dynamic learning rate, conjugate gradient, improved error function. Among them, the dynamic learning rate, it reaches the learning rate of the hidden layer and output layer; Conjugate gradient, this paper gives three calculating formula; Improved error function, to solve different problems are also given in three types of error function. BP learning algorithm in this paper, the research contents, make the convergence stability, convergence speed, initial value sensitivity, it has good effect, which has large significant in terms of academic and applied significance.

Application of BP Neural Network in Products Forecast

2014 ◽  
Vol 644-650 ◽  
pp. 2455-2458
Author(s):  
Cai Xia Liu
Keyword(s):  
Neural Network ◽  
Parallel Processing ◽  
Bp Neural Network ◽  
Structural Model ◽  
Learning Rate ◽  
Practical Significance ◽  
Nonlinear Mapping ◽  
Bp Network ◽  
Hidden Layer ◽  
Design Ideas

BP neural network has parallel processing capabilities and a good approximation of the nonlinear mapping and gradually been widely used in the forecast. Because it is difficult to determine the BP neural network structural model, this paper presents the design ideas from six aspects. Combined with the practical example-mushroom classification, this paper presents the affect of the hidden layer, learning rate, training function on BP network and has some practical significance.

A Simulation and Prediction Method of the Height of Wheat Based on BP Neural Networks and the Ant Colony Algorithm

2013 ◽  
Vol 659 ◽  
pp. 113-117 ◽  
Author(s):  
Hong Yi Li ◽  
Xi Xin Wu ◽  
Tong Wang ◽  
Di Zhao
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Bp Neural Network ◽  
Ant Colony Algorithm ◽  
Prediction Method ◽  
Ant Colony ◽  
Local Optimum ◽  
Prediction Problem ◽  
Bp Neural Networks ◽  
Advantages And Disadvantages

This paper focuses on the simulation and prediction problem of height of the crops, particularly the wheat, which plays a significantly important role in its yield, in different growing stages. Our model bases on the BP neural network and the ant colony algorithm. Both of these two algorithms has their own advantages and disadvantages. However, through observations, we find that their advantages and disadvantages seem to be complementary, by which we propose the combination algorithm. This combination algorithm can conquer the local optimum problem of the BP Neural Network, and could overcome the shortcomings of the weak local optimum searching capability of the ant colony algorithm. The experiments show that the our proposed algorithm can hopefully yield good simulation and prediction results of the height of wheat in different growing stages.

Sign in / Sign up

Export Citation Format

Share Document