Application of BP Neural Network Based on Genetic Algorithm in Quantitative Analysis of Mixed GAS

2015 ◽  
Author(s):  
Hongyan Chen ◽  
Wenzhen Liu ◽  
Jian Qu ◽  
Bing Zhang ◽  
Zhibin Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Quantitative Analysis ◽  
Bp Neural Network ◽  
Mixed Gas

Quantitative Analysis of Carbon with Laser-Induced Breakdown Spectroscopy (LIBS) Using Genetic Algorithm and Back Propagation Neural Network Models

2019 ◽  
Vol 73 (6) ◽  
pp. 678-686 ◽  
Author(s):  
Jiao He ◽  
Congyuan Pan ◽  
Yongbin Liu ◽  
Xuewei Du
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Quantitative Analysis ◽  
Emission Line ◽  
Carbon Content ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Laser Induced Breakdown Spectroscopy ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

Carbon content detection is an essential component of the metal-smelting and classification processes. An obstacle in carbon content detection by laser-induced breakdown spectroscopy (LIBS) of steel is the interference of carbon lines by the adjacent Fe lines. The emission line of C(I) 247.86 nm generally has higher response and transmission efficiency than the emission line of C(I) 193.09 nm, but it blends with the Fe(II) 247.86 nm line. Therefore, this study proposes a method of back propagation (BP) neural network modeling, which incorporates a genetic algorithm (GA), evaluates the method of parameter modeling and prediction based on GA to optimize the BP neural network (GA–BP), and realizes a quantitative analysis of the C(I) 247.86 nm line. The achieved root mean square error for the GA–BP model is 0.0114. The obtained linear correlation coefficient shows a significant improvement after correction, indicating that the proposed method is effective. The method is concise, easy to implement, and can be applied in the carbon content detection of steels and iron-based alloys.

Transformer Fault Diagnosis Based on Improved Quantum Genetic Algorithm and BP Network

2010 ◽  
Vol 29-32 ◽  
pp. 1543-1549 ◽  
Author(s):  
Jie Wei ◽  
Hong Yu ◽  
Jin Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Fuzzy Theory ◽  
Back Propagation ◽  
Gas Analysis ◽  
Bp Network ◽  
Quantum Genetic Algorithm ◽  
Transformer Fault

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.

scholarly journals Forecast of Waterway Cargo Turnover Volume Based on Genetic Algorithm to Optimize Neural Network Parameters

2021 ◽  
Vol 2083 (3) ◽  
pp. 032010
Author(s):  
Rong Ma
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Forecast Model ◽  
Network Parameters ◽  
The Neural Network ◽  
Target Effect ◽  
The Empirical Analysis ◽  
Better Than

Abstract The traditional BP neural network is difficult to achieve the target effect in the prediction of waterway cargo turnover. In order to improve the accuracy of waterway cargo turnover forecast, a waterway cargo turnover forecast model was created based on genetic algorithm to optimize neural network parameters. The genetic algorithm overcomes the trap that the general iterative method easily falls into, that is, the “endless loop” phenomenon that occurs when the local minimum is small, and the calculation time is small, and the robustness is high. Using genetic algorithm optimized BP neural network to predict waterway cargo turnover, and the empirical analysis of the waterway cargo turnover forecast is carried out. The results obtained show that the neural network waterway optimized by genetic algorithm has a higher accuracy than the traditional BP neural network for predicting waterway cargo turnover, and the optimization model can long-term analysis of the characteristics of waterway cargo turnover changes shows that the prediction effect is far better than traditional neural networks.

Application of Blow-off Wind Tunnel Control Based on Genetic Algorithm Optimized BP-Neural Network PID Neural Network

2013 ◽  
Vol 310 ◽  
pp. 557-559 ◽  
Author(s):  
Li Ji ◽  
Xiao Fei Lian
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Wind Tunnel ◽  
Bp Neural Network ◽  
Control Method ◽  
Network Control ◽  
Operating Conditions ◽  
Algorithm Optimization ◽  
Large Delay ◽  
Neural Network Pid

For a blow-off tunnel running, there is the large delay and lag issues. We build a mathematical model of the wind tunnel Mach number control by the test modeling method, then analyse the pros and cons of various control methods based on BP neural network control algorithm. Put forward genetic algorithm optimization neural network adaptive control method to solve the large inertia of the wind tunnel system, and large delay. A large number of simulation studies, run a variety of operating conditions for the wind tunnel simulation proved that the improved adaptive neural network PID control method is reasonable and effective.

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