Research of Evaluation System of English Autonomous Learning Ability Based on BP Neural Network

2015 ◽  
Vol 10 (12) ◽  
pp. 125-134
Author(s):  
Bo Du ◽  
Yan Zhang ◽  
Jing Zhang ◽  
Dongming Li
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Evaluation System ◽  
Autonomous Learning ◽  
Learning Ability

Study for Workpiece Identity Based on the BP Neural Network and the Zernike Moment

2013 ◽  
Vol 694-697 ◽  
pp. 1958-1963 ◽  
Author(s):  
Xian Wei ◽  
Jing Dong Zhang ◽  
Xue Mei Qi
Keyword(s):  
Neural Network ◽  
Fault Tolerance ◽  
Parallel Processing ◽  
Bp Neural Network ◽  
Complex Shape ◽  
Learning Ability ◽  
Zernike Moment ◽  
Recognition Ability ◽  
Identity Based ◽  
Self Learning

The robots identify, locate and install the workpiece in FMS system by identifying the characteristic information of target workpiece. The paper studied the recognition technology of complex shape workpiece with combination of BP neural network and Zernike moment. The strong recognition ability of Zernike moment can extract the characteristic. The good fault tolerance, classification, parallel processing and self-learning ability of BP neural network can greatly improve the accurate rate of recognition. Experimental results show the effectiveness of the proposed method.

scholarly journals Research of Students Learning Quality Evaluation System Based on BP Neural Network

2018 ◽  
Vol 1069 ◽  
pp. 012120
Author(s):  
Li Dai ◽  
Yuexiang Fan ◽  
Qi Lu ◽  
Mei Lin ◽  
Li Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Evaluation System ◽  
Quality Evaluation ◽  
Quality Evaluation System

Design of Vehicle’s Controlling System Used a 4WS Control Method Based on BP Neural Network

2011 ◽  
Vol 201-203 ◽  
pp. 276-280
Author(s):  
Ya Peng Liu ◽  
Yan Tang ◽  
Jia Bin Bi
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Control Method ◽  
Theoretical Method ◽  
Learning Ability ◽  
Controlling System ◽  
Nonlinear Dynamic Characteristics ◽  
Tokyo Institute ◽  
Institute Of Technology ◽  
Technology Institute

In this paper, a 4WS control method based on BP neural network was introduced. It used the BP neural network to simulate the map of vehicle and the nonlinear dynamic characteristics of the tire to avoid large errors that relying on mathematical simulation model of the problem. The 4WS measured data of Tokyo institute of Technology institute of Japan was used and used BP neural network method to identify the nonlinear characteristics of vehicle and tires. System controller’s design is not based on any theoretical method, but on the BP neural network’s self-learning ability. Experimental results show that this method has good controlling characteristics, and it can improve the vehicle’s active safety and manipulating stability effectively.

PBP: A Type of BP Neural Network Algorithm Based on Priori Triggering

2011 ◽  
Vol 271-273 ◽  
pp. 441-447
Author(s):  
Xiao Mei Chen ◽  
Dang Gang ◽  
Tian Yang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Large Scale ◽  
Learning Ability ◽  
Traffic Flows ◽  
It Use ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Large Scale Networks ◽  
High Level

The algorithm of anomaly detection for large scale networks is a key way to promptly detect the abnormal traffic flows. In this paper, priori triggered BP neural network algorithm(PBP) is analyzed for the purpose of dealing with the problems caused by typical algorithms that are not able to adapt and learn; detect with high precision; provide high level of correctness. PBP uses K-Means and PCA to trigger self-adapting and learning ability, and also, it uses historical neuron parameter to initialize the neural network, so that it use the trained network to detect the abnormal traffic flows. According to experiments, PBP can obtain a higher level of correctness of detection than priori algorithm, and it can adapt itself according to different network environments.

Study on Chaotic Time Series Prediction Algorithm for Kent Mapping Based on Particle Swarm Optimization

2014 ◽  
Vol 511-512 ◽  
pp. 941-944 ◽  
Author(s):  
Hong Li Bian
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Particle Swarm Optimization ◽  
Bp Neural Network ◽  
Time Series Prediction ◽  
Particle Swarm ◽  
Chaotic Time Series ◽  
Learning Ability ◽  
Swarm Optimization ◽  
Chaotic Time Series Prediction

Based on the particle swarm optimization (PSO) and BP neural network (BPNN), an algorithm for BP neural network optimized particle swarm optimization (PSOBPNN) is proposed. In the algorithm, PSO is used to obtain better network initial threshold and weight to compensate the defect of connection weight and thresholds of BPNN, thus it can make BPNN have faster convergence and greater learning ability. The efficiency of the proposed prediction method is tested by the simulation of the chaotic time series for Kent mapping. The simulations results show that the proposed method has higher forecasting accuracy compared with the BPNN, so it is proved that the algorithm is feasible and effective in the chaotic time series prediction.

Sign in / Sign up

Export Citation Format

Share Document