Optimizing Design and Application of BP Neural Networks Based on Genetic Algorithm

2011 ◽  
Vol 317-319 ◽  
pp. 245-249
Author(s):  
Wang Jie Niu ◽  
Er Guang Qu ◽  
Chun Yan Liu
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Learning Rate ◽  
Learning Ability ◽  
Bp Neural Networks ◽  
Inertia Coefficient ◽  
Optimizing Design ◽  
Self Learning ◽  
Design And Application

Genetic algorithm optimize weight’s volume of Neural Networks by optimizing learning rate and inertia coefficient, which overcome the BP algorithmic shortcoming of easy into the part extreme, and have ensured BP algorithmic training accuracy, and makes it have higher self-adaptability and self-learning ability.

Improved Artificial BP Neural Networks and Genetic Algorithm Used to Predict the Purity of the Artificial Synthetic Hydrotalcite

2007 ◽  
Vol 280-283 ◽  
pp. 495-498
Author(s):  
Qiang Luo ◽  
Qing Li Ren
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Prediction Model ◽  
Process Parameters ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Raw Material ◽  
Algorithm Analysis ◽  
Neural Net ◽  
Bp Neural Networks

A prediction model for purity of the artificial synthetic hydrotalcite under varied process parameters based on improved artificial back-propagation (BP) neural networks is developed. And the non-linear relationship between the hydrotalcite purity and the raw material adding amount of NaOH, MgCl2 and AlCl3 was established based on BP learning algorithm analysis and convergence improvement. The hydrotalcite purity can be predicted by means of the trained neural net. Thus, by virtue of the prediction model, the future hydrotalcite purity can be evaluated under random complicated raw material amounts. Moreover, the best processing technology is optimized using the genetic algorithm.

Neural-Network Based Automatic PID Gain Tuning in the Presence of Time-Varying Disturbances in Hard Disk Drives

2013 ◽  
Author(s):  
Liting Sun ◽  
Xu Chen ◽  
Masayoshi Tomizuka
Keyword(s):  
Neural Networks ◽  
Hard Disk Drives ◽  
Hard Disk ◽  
Disk Drive ◽  
Learning Ability ◽  
Pid Controllers ◽  
Time Varying ◽  
Control Scheme ◽  
Frequency Components ◽  
Self Learning

In hard disk drive (HDD) systems, disturbances commonly contain different frequency components that are time-varying in nature. Different HDD systems may subject to different excitation disturbances. In this case, it is difficult for fixed-gain PID controllers to maintain a good overall performance. When the characteristics of the disturbances change, or when servos are designed for different drive products, the PID gains have to be retuned. This paper presents automatic online gain tuning of PID controllers based on neural networks. The proposed control scheme can automatically adjust the PID parameters online in the presence of time-varying disturbances, or for different disturbances among different HDD products, and find the optimal sets of PID gains through the self-learning ability of neural networks.

Short-Term Gas Load Forecasting Based on Wavelet BP Neural Network Optimized by Genetic Algorithm

2014 ◽  
Vol 631-632 ◽  
pp. 79-85 ◽  
Author(s):  
Feng Yu ◽  
Zhi Qing Wang ◽  
Xiao Zhong Xu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Load Forecasting ◽  
Learning Ability ◽  
Time Frequency ◽  
Proposed Model ◽  
Real Coded Genetic Algorithm ◽  
Self Learning ◽  
Single Neural Network

Aiming at the limitations of a single neural network for effective gas load forecasting, a combinational model based on wavelet BP neural network optimized by genetic algorithm is proposed. The problems that traditional BP algorithm converges slowly and easily falls into local minimum are overcame. The wavelet neural network strengthens the function approximation capacity of the network by combining the well time-frequency local feature of wavelet transform with the self-learning ability of neural network. And optimized by the real coded genetic algorithm, the network converges more quick than non-optimized one. This proposed model is applied to daily gas load forecasting for Shanghai and the simulation results indicate that this algorithm has excellent prediction effect.

Vehicle Type Recognition Using in Geomagnetic Data Based on Neural Network

2013 ◽  
Vol 313-314 ◽  
pp. 1380-1384
Author(s):  
Rui Qing Kang ◽  
Xi Sheng Li ◽  
Hai Jian Wang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Recognition Rate ◽  
Intelligent Transportation ◽  
Geomagnetic Disturbance ◽  
Bp Neural Networks ◽  
Vehicle Type ◽  
Geomagnetic Data

Vehicle Type Recognition is the base and key point for Intelligent Transportation,Through the geomagnetic disturbance data of different vehicle type, constituting a sort of BP neural networks, and optimizing it using Genetic Algorithm. The result is good. This method can raise the recognition rate effectively and reduce the quantity of calculating. It has strong practicability.

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