scholarly journals Iris Image Real-Time Pre-estimation Using Compound BP Neural Network

2005 ◽  
pp. 450-456 ◽  
Author(s):  
Xueyi Ye ◽  
Peng Yao ◽  
Fei Long ◽  
Zhenquan Zhuang
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Bp Neural Network ◽  
Iris Image

Angular Velocity Prediction of GFSINS Based on BP Neural Network

2012 ◽  
Vol 452-453 ◽  
pp. 846-852
Author(s):  
Hai Qing Duan ◽  
Qi Dan Zhu
Keyword(s):  
Neural Network ◽  
Angular Velocity ◽  
Real Time ◽  
Bp Neural Network ◽  
Neural Cells ◽  
Bp Network ◽  
Velocity Prediction ◽  
Sample Interval ◽  
Neural Network Algorithm ◽  
Configuration Scheme

Aiming at low precision for traditional angular velocity algorithms in GFSINS, a BP neural network algorithm without complex mathematic computation is put forward to calculate angular velocity. Based on a ten-accelerometer configuration scheme, the accelerometer output, sample interval and fixed position are chosen as input, angular velocity got by lognormal algorithm is chosen as output, and 5000 sample data is trained in the several conditions with different hiding layers, neural cells and training steps. Then a three-layer BP network model with 30 hiding layer neural cells is built. Finally, the angular velocity is predicted in real time by the model. Results show that network has strong adaptive capability and real time, and compared with lognormal algorithm, prediction time is almost equal, but prediction precision of angular velocity is nearly improved by three times.

scholarly journals Connected Vehicle as a Mobile Sensor for Real Time Queue Length at Signalized Intersections

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2059 ◽  
Author(s):  
Kai Gao ◽  
Farong Han ◽  
Pingping Dong ◽  
Naixue Xiong ◽  
Ronghua Du
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Bp Neural Network ◽  
Queue Length ◽  
Penetration Rate ◽  
Connected Vehicles ◽  
Connected Vehicle ◽  
Mixed Traffic ◽  
Model Based ◽  
Sensing Model

With the development of intelligent transportation system (ITS) and vehicle to X (V2X), the connected vehicle is capable of sensing a great deal of useful traffic information, such as queue length at intersections. Aiming to solve the problem of existing models’ complexity and information redundancy, this paper proposes a queue length sensing model based on V2X technology, which consists of two sub-models based on shockwave sensing and back propagation (BP) neural network sensing. First, the model obtains state information of the connected vehicles and analyzes the formation process of the queue, and then it calculates the velocity of the shockwave to predict the queue length of the subsequent unconnected vehicles. Then, the neural network is trained with historical connected vehicle data, and a sub-model based on the BP neural network is established to predict the real-time queue length. Finally, the final queue length at the intersection is determined by combining the sub-models by variable weight. Simulation results show that the sensing accuracy of the combined model is proportional to the penetration rate of connected vehicles, and sensing of queue length can be achieved even in low penetration rate environments. In mixed traffic environments of connected vehicles and unconnected vehicles, the queuing length sensing model proposed in this paper has higher performance than the probability distribution (PD) model when the penetration rate is low, and it has an almost equivalent performance with higher penetration rate while the penetration rate is not needed. The proposed sensing model is more applicable for mixed traffic scenarios with much looser conditions.

Harmonic Monitoring Based on BP Neural Network

2012 ◽  
Vol 532-533 ◽  
pp. 1354-1358
Author(s):  
Bai Fen Liu ◽  
Yun Chen ◽  
Xian Wu Fang
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Bp Neural Network ◽  
Network Theory ◽  
Harmonic Suppression ◽  
Matlab Simulation ◽  
Time Data ◽  
Monitoring Model ◽  
Training Capacity ◽  
Neural Network Theory

This article applies the BP neural network theory into the measuring of harmonics, through the model automatically learn training capacity to create real-time monitoring model of harmonic. And that I am using matlab simulation, to obtained harmonic simulation data. Through this simulation technology to monitor the harmonic and obtain real-time data, the data can provide reference for harmonic suppression.

Real-time Strength Prediction of Different Types of Concrete Based on BP Neural Network

2018 ◽  
Vol 16 (6) ◽  
Author(s):  
Zhuo Yang ◽  
Mengxiong Tang ◽  
Xuan Ji ◽  
Hesong Hu
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Bp Neural Network ◽  
Strength Prediction ◽  
Different Types ◽  
Time Strength

Research of BP Neural Network Algorithm Testing Platform Based on OPC Communication

2014 ◽  
Vol 668-669 ◽  
pp. 575-580
Author(s):  
Bing Wu Chen ◽  
Xing Chen ◽  
Hui Peng Shu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Real Time ◽  
Bp Neural Network ◽  
Data Exchange ◽  
Communication Process ◽  
Effective Control ◽  
Testing Platform ◽  
Network Algorithm ◽  
Neural Network Algorithm

Effective control algorithm can be applied to the real-time control system by the data exchange between MCGS and MATLAB with OPC technology, as a result, a testing platform for advanced control algorithms is established. The paper presents a second-order liquid level control system for research example; the BP neural network algorithm is applied to the control system. The communication process verifies that the data exchange is reliable and the simulation results show the control of the BP neural network algorithm for real-time optimized control process is effective.

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