scholarly journals Research on Gaussian Plume Model of Gas Diffusion in Coal Mine Roadway Based on BP Neural Network Optimized by Genetic Algorithm

Author(s):  
Zhen Nie ◽  
Hongwei Ma ◽  
Yishu Zhang
2012 ◽  
Vol 605-607 ◽  
pp. 1605-1608
Author(s):  
Yang Yang He ◽  
Zhi Gang Niu

This thesis regards TUT-CMDR type coal mine detection robots as the research object and put forward an application of optimized BP neural network based on Quantum Genetic Algorithm in PID Control of motor speed. Transfer function model of speed control system of TUT-CMDR motor was established. Firstly, initial weights and thresholds of BP neural network were optimized by Quantum Genetic Algorithm, and then BP neural network was designed to adjust the parameters of PID on line. Finally, the results show that the algorithm is feasible and superiority.


2010 ◽  
Vol 29-32 ◽  
pp. 1543-1549 ◽  
Author(s):  
Jie Wei ◽  
Hong Yu ◽  
Jin Li

Three-ratio of the IEC is a convenient and effective approach for transformer fault diagnosis in the dissolved gas analysis (DGA). Fuzzy theory is used to preprocess the three-ratio for its boundary that is too absolute. As the same time, an improved quantum genetic algorithm IQGA (QGASAC) is used to optimize the weight and threshold of the back propagation (BP). The local and global searching ability of the QGASAC approach is utilized to find the BP optimization solution. It can overcome the slower convergence velocity and hardly getting the optimization of the BP neural network. So, aiming at the shortcoming of BP neural network and three-ratio, blurring the boundary of the gas ratio and the QGASAC algorithm is introduced to optimize the BP network. Then the QGASAC-IECBP method is proposed in this paper. Experimental results indicate that the proposed algorithm in this paper that both convergence velocity and veracity are all improved to some extent. And in this paper, the proposed algorithm is robust and practical.


Sign in / Sign up

Export Citation Format

Share Document