Flight Off-Block Time Prediction Based on BP Neural Network Optimized by Genetic Algorithm

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.

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Travel time prediction of urban road based on BP neural network

Journal of Physics Conference Series ◽

10.1088/1742-6596/1651/1/012190 ◽

2020 ◽

Vol 1651 ◽

pp. 012190

Author(s):

Fangyi Deng ◽

Pei Su ◽

Bingxue Luo ◽

Peng Wu ◽

Yan Guo

Keyword(s):

Neural Network ◽

Travel Time ◽

Bp Neural Network ◽

Travel Time Prediction ◽

Urban Road ◽

Time Prediction

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

Journal of Physics Conference Series ◽

10.1088/1742-6596/2083/3/032010 ◽

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.

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.310.557 ◽

2013 ◽

Vol 310 ◽

pp. 557-559 ◽

Cited By ~ 2

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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