Probing modification of BP neural network learning-rate

2003 ◽  
Author(s):  
Pan Hao ◽  
Jing-Ling Yuan ◽  
Luo Zhong
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Learning Rate ◽  
Neural Network Learning ◽  
Network Learning

scholarly journals The Application of BP Neural Network in Internet of Things

2012 ◽  
Vol 6-7 ◽  
pp. 1098-1102 ◽  
Author(s):  
Dan Dan Cui ◽  
Fei Liu
Keyword(s):  
Neural Network ◽  
Internet Of Things ◽  
Bp Neural Network ◽  
Learning Algorithm ◽  
Neural Network Learning ◽  
Network Learning ◽  
Input Layer ◽  
Telecommunications Network ◽  
Hidden Layer ◽  
The Internet Of Things

BP algorithm is a typical artificial neural network learning algorithm, the main structure consists of an input layer, one or more hidden layer, an output layer, the layers of the number of neurons, the output of each node the value is decided by the input values, the role, function and threshold. The Internet of Things is based on the information carrier of the traditional telecommunications network, so that all can be individually addressable ordinary physical objects to achieve the interoperability network. The paper puts forward the application of BP neural network in internet of things. The experiment shows BP is superior to RFID in internet of things.

scholarly journals System Identification of Neural Signal Transmission Based on Backpropagation Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Xiangyu Li ◽  
Chunhua Yuan ◽  
Bonan Shan
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Signal Transmission ◽  
Neural Model ◽  
Neural Firing ◽  
Output Data ◽  
Neural Network Learning ◽  
Network Learning ◽  
Network Training ◽  
Input And Output

The identification method of backpropagation (BP) neural network is adopted to approximate the mapping relation between input and output of neurons based on neural firing trajectory in this paper. In advance, the input and output data of neural model is used for BP neural network learning, so that the identified BP neural network can present the transfer characteristics of the model, which makes the network precisely predict the firing trajectory of the neural model. In addition, the method is applied to identify electrophysiological experimental data of real neurons, so that the output of the identified BP neural network can not only accurately fit the neural firing trajectories of neurons participating in the network training but also predict the firing trajectories and spike moments of neurons which are not involved in the training process with high accuracy.

A Parallel BP Neural Network Based on the FPGA

2012 ◽  
Vol 239-240 ◽  
pp. 1541-1545 ◽  
Author(s):  
Yu Hui Guo ◽  
De Tai Zhou ◽  
Xiang Ping Qian ◽  
Xian Qiang Zeng
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Software System ◽  
Neural Network Learning ◽  
Network Learning ◽  
System On Programmable Chip ◽  
Computer Unit

This paper, with the analysis of BP neural network learning and execution algorithm on single computer unit, a parallel neural network on many computer units is constructed, a system on programmable chip based on FPGA and uClinux is provided. Because it’s flexibility and reliability, this parallel neural network can be widely used where there is a large quantity of data to be processed. In addition to it, the system based on the SOPC has good versatility and easy to transplant because the reconfiguration of the hardware logic and software system.

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 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

The Application of BP Neural Network Learning Algorithm Based on the Particle Swarm Optimization

2013 ◽  
Vol 706-708 ◽  
pp. 2057-2062
Author(s):  
Zhi Hong Sun ◽  
Jun Wang ◽  
Bao Ji Xu
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Real Estate ◽  
Bp Neural Network ◽  
Learning Algorithm ◽  
Particle Swarm ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Neural Network Learning ◽  
Network Learning

The development of real estate has been affected by various social factors, including economic factors. BP neural network can more accurately forecast the trend of real estate industry according to economic development indicators. But BP neural network is slow convergence in the training process, and easily falls into local optimum. The BP neural network learning algorithm based on the particle swarm optimization (PSO) optimizes the weights and thresholds of the network by PSO algorithm, then to train BP neural network. The experimental results show that the performance of this new algorithm is better than BP neural network, but also has good convergence.

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