Character Image Recognition Based on the Improved BP Neural Network

2011 ◽  
Vol 58-60 ◽  
pp. 2655-2658 ◽  
Author(s):  
Hong Zhao
Keyword(s):  
Neural Network ◽  
Computer Simulation ◽  
Image Recognition ◽  
Bp Neural Network ◽  
Bp Algorithm ◽  
Simulation Results ◽  
Improved Bp Algorithm

This paper raises a kind of improved BP algorithm in order to compensate for some shortcomings which exist in traditional BP neural network. It has been applied to the recognition of character images. Computer simulation results demonstrate that it does bring about an ideal result.

Research on BP Neural Network Algorithm Based on Quasi-Newton Method

2014 ◽  
Vol 686 ◽  
pp. 388-394 ◽  
Author(s):  
Pei Xin Lu
Keyword(s):  
Neural Network ◽  
Empirical Analysis ◽  
Newton Method ◽  
Bp Neural Network ◽  
Bp Algorithm ◽  
Newton Methods ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Quasi Newton ◽  
Improved Bp Algorithm

With more and more researches about improving BP algorithm, there are more improvement methods. The paper researches two improvement algorithms based on quasi-Newton method, DFP algorithm and L-BFGS algorithm. After fully analyzing the features of quasi-Newton methods, the paper improves BP neural network algorithm. And the adjustment is made for the problems in the improvement process. The paper makes empirical analysis and proves the effectiveness of BP neural network algorithm based on quasi-Newton method. The improved algorithms are compared with the traditional BP algorithm, which indicates that the improved BP algorithm is better.

Research on the Improvement of BP Neural Network Algorithm and its Application

2014 ◽  
Vol 926-930 ◽  
pp. 3216-3219 ◽  
Author(s):  
Xiong Kai
Keyword(s):  
Neural Network ◽  
Optimization Algorithm ◽  
Bp Neural Network ◽  
Bp Algorithm ◽  
Network Algorithm ◽  
Simulation Calculation ◽  
Neural Network Algorithm ◽  
Working Principle ◽  
Calculation Ability ◽  
Improved Bp Algorithm

BP neural network has excellent ability to solve nonlinear optimal problem for its powerful simulation calculation ability and is wildly used in various disciplines in recent years, but its shortages of low convergence speed and falling into local minimum easily limit the usage of the algorithm. Based on the Levenberg-Marqardt optimization algorithm, the paper improves the BP neural network to overcome its shortages. First, the working structure and shortages of BP neural network are analyzed with more details. Second, the working principle of BP neural network algorithm is improved with Levenberg-Marqardt optimization algorithm and the calculation flows is redesigned. Third, data from three universities are used to realize the improved algorithm and the experimental results shows that the improved BP algorithm has better performance in calculation time and evaluation accuracy when used in university classroom teaching evaluation practically.

A FUZZY NEURAL NETWORK FOR THE ACTIVE VIBRATION CONTROL OF A CENTRIFUGAL PENDULUM VIBRATION ABSORBER

2009 ◽  
Vol 20 (12) ◽  
pp. 1963-1979
Author(s):  
CHI-HSIUNG LIANG ◽  
PI-CHENG TUNG
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Active Vibration Control ◽  
Bp Algorithm ◽  
Vibration Absorber ◽  
Neural Network Controller ◽  
Network Controller ◽  
Centrifugal Pendulum Vibration Absorber ◽  
Active Vibration ◽  
Simulation Results

In this study, we develop a fuzzy back-propagation (BP) neural network controller for active vibration control of a centrifugal pendulum vibration absorber (CPVA). The fuzzy BP neural network controller systems can be viewed as a conventional fuzzy algorithm for coarse tuning. The BP algorithm can also be applied for fine tuning, in this case to regulate the anti-resonance frequency in an active pendulum vibration absorber (APVA), by suppressing vibration of the carrier. The dynamic model of the APVA was developed and simulated using MATLAB. In the simulation results, when the frequency of the disturbance changes, the outputs of the fuzzy BP neural network controller are used to determine an appropriate value for the torque of the active pendulum such that the vibration amplitude of the carrier is minimized. A comparison of the carrier vibration results for the CPVA, the fuzzy algorithm and the fuzzy BP algorithm is performed. The simulation results demonstrate the effectiveness of the proposed fuzzy BP neural network APVA for reducing the carrier vibrations.

Temperature Drift Compensation Algorithm Based on BP and GA in Quartzes Flexible Accelerometer

2012 ◽  
Vol 249-250 ◽  
pp. 95-99 ◽  
Author(s):  
Xue Fei Li ◽  
Deng Hua Li ◽  
Jing Min Gao ◽  
Mei Sa Pang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Bp Algorithm ◽  
Good Effect ◽  
Theory Analysis ◽  
Temperature Drift ◽  
Different Temperatures ◽  
Drift Compensation ◽  
Improved Bp Algorithm

For the purpose of reducing the drift of the Quartzes Flexible accelerometer outputs in different temperatures, an improved BP algorithm based on GA (Genetic Algorithm) for temperature drift compensation of Quartzes Flexible accelerometer is studied in this paper. Based on the theory analysis and the simulation, the GA model has less training steps and better fitting precision compared with BP Neural Network. The results show that this method on temperature drift compensation of quartz flexible accelerometer has achieved good effect.

scholarly journals Macro Modeling of V-Shaped Electro-Thermal MEMS Actuator with Human Error Factor

Micromachines ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 622
Author(s):  
Dongpeng Zhang ◽  
Anjiang Cai ◽  
Yulong Zhao ◽  
Tengjiang Hu
Keyword(s):  
Neural Network ◽  
Simulation Model ◽  
Bp Neural Network ◽  
Human Error ◽  
Calculation Model ◽  
Fuzzy Mathematics ◽  
Ansys Simulation ◽  
Macro Modeling ◽  
Error Factor ◽  
Simulation Results

The V-shaped electro-thermal MEMS actuator model, with the human error factor taken into account, is presented in this paper through the cascading ANSYS simulation model and the Fuzzy mathematics calculation model. The Fuzzy mathematics calculation model introduces the human error factor into the MEMS actuator model by using the BP neural network, which effectively reduces the error between ANSYS simulation results and experimental results to less than 1%. Meanwhile, the V-shaped electro-thermal MEMS actuator model, with the human error factor included, will become more accurate as the database of the V-shaped electro-thermal actuator model grows.

scholarly journals Classification of ETM+ Remote Sensing Image Based on Hybrid Algorithm of Genetic Algorithm and Back Propagation Neural Network

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Haisheng Song ◽  
Ruisong Xu ◽  
Yueliang Ma ◽  
Gaofei Li
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Genetic Algorithm ◽  
Hybrid Algorithm ◽  
Local Minimum ◽  
Back Propagation ◽  
Remote Sensing Image ◽  
Back Propagation Neural Network ◽  
Bp Algorithm ◽  
Improved Bp Algorithm

The back propagation neural network (BPNN) algorithm can be used as a supervised classification in the processing of remote sensing image classification. But its defects are obvious: falling into the local minimum value easily, slow convergence speed, and being difficult to determine intermediate hidden layer nodes. Genetic algorithm (GA) has the advantages of global optimization and being not easy to fall into local minimum value, but it has the disadvantage of poor local searching capability. This paper uses GA to generate the initial structure of BPNN. Then, the stable, efficient, and fast BP classification network is gotten through making fine adjustments on the improved BP algorithm. Finally, we use the hybrid algorithm to execute classification on remote sensing image and compare it with the improved BP algorithm and traditional maximum likelihood classification (MLC) algorithm. Results of experiments show that the hybrid algorithm outperforms improved BP algorithm and MLC algorithm.

Application of BP Neural Network PID Algorithm in Refrigerated Space Temperature Control

2014 ◽  
Vol 599-601 ◽  
pp. 827-830 ◽  
Author(s):  
Wei Tian ◽  
Yi Zhun Peng ◽  
Pan Wang ◽  
Xiao Yu Wang
Keyword(s):  
Neural Network ◽  
Temperature Control ◽  
Bp Neural Network ◽  
Pid Control ◽  
Bp Algorithm ◽  
Pid Algorithm ◽  
Controller Performance ◽  
Neural Network Algorithm ◽  
Adaptive Ability ◽  
Neural Network Pid

Taking the temperature control of a refrigerated space as example, this paper designs a controller which is based on traditional PID operation and BP neural network algorithm. It has better steady-state precision and adaptive ability. Firstly, the article introduces the concepts of the refrigerated space, PID and BP algorithm. Then, the temperature control of refrigerated space is simulated in MATLAB. The PID parameters will be adjusted by simulation in BP Neural Network. The PID control parameters could be created real-time online, which makes the controller performance best.

scholarly journals A modified NK algorithm based on BP neural network and DEMATEL for evolution path optimization of urban innovation ecosystem

2021 ◽  
Author(s):  
Ruijian Liu ◽  
Fangcheng Tang ◽  
Yuhan Wang ◽  
Shaofang Zheng
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Urban Governance ◽  
Innovation Network ◽  
Governance System ◽  
Innovation Ecosystem ◽  
Governance Capacity ◽  
New Era ◽  
Evolution Path ◽  
Simulation Results

AbstractIn the new era, the key measure to accelerate the construction of smart city, so as to promote the modernization of urban governance system and governance capacity, is to establish a good urban innovation ecosystem, and guide its continuous evolution to the direction of the highest efficiency and the best performance. Focusing on solving the practical problem of “how the urban innovation ecosystem evolves”, this paper develops a NK algorithm using BP neural network and DEMATEL method. First, through literature research, constructing the urban innovation ecosystem including five subsystems of innovation talents, innovation subjects, innovation resources, innovation environment and innovation network. Then, taking Beijing as an example, the weights and the number of epistatic relationships of each subsystem in its innovation ecosystem are calculated by BP neural network and DEMATEL method, and the NK model is modified; on this basis, the fitness values corresponding to different states of the system are calculated using MATLAB software, and the optimal evolution path of Beijing innovation ecosystem is determined through the comparison of 100,000 simulation results. The results show that the optimal evolution path of Beijing's innovation ecosystem is to create a favorable environment and culture for innovation first; then increase the input of innovation resources; and then promote the development of innovation network assets; on this basis, cultivate, attract and retain innovative talents; and finally strengthen the construction of innovation subjects.

Sign in / Sign up

Export Citation Format

Share Document