scholarly journals Genetic Algorithm to Optimize the Design of High Temperature Protective Clothing Based on BP Neural Network

2021 ◽  
Vol 9 ◽  
Author(s):  
Feng Xu ◽  
Ling-Yu Mo ◽  
Hong Chen ◽  
Jia-Ming Zhu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
High Temperature ◽  
Bp Neural Network ◽  
Protective Clothing ◽  
Transfer Model ◽  
Optimal Parameters ◽  
Clothing Design ◽  
Thickness Function ◽  
High Temperature Operation

For the clothing design for high-temperature operation, the theory or method such as partial differential, nonlinear programming and finite difference method was first applied to construct the overall heat transfer model of “high temperature environment--clothing--air layer--skin” and draw the temperature distribution map. Secondly, according to the human body burn model, the optimal parameters of fabric thickness are obtained preliminarily. Finally, the weights and thresholds of BP neural network were optimized by genetic algorithm, and these optimized values were assigned to the optimized BP neural network, and the nonlinear thickness function was approximated and optimized with MATLAB.

Application of Neural Network Based on Genetic Algorithm to Predict Hypotension Events during Spinal Anesthesia

2012 ◽  
Vol 542-543 ◽  
pp. 1394-1397
Author(s):  
Yu Qiu ◽  
Hai Jun Dai
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Global Optimization ◽  
General Anesthesia ◽  
Bp Neural Network ◽  
Simulation Experiment ◽  
Optimal Parameters ◽  
Accuracy Requirement ◽  
Artificial Neural Network Ann

An accurate and efficient artificial neural network (ANN) based genetic algorithm (GA) is presented for predicting hypotension during general anesthesia. The genetic algorithm global optimization characteristics are used to optimize the BP neural network weights, and learning samples are trained and modeled by BP neural network with optimal parameters. The simulation experiment is carried out with MATLAB. The result indicated that the model forecasting results are close with the actual results and meet the accuracy requirement to General Anesthesia.

scholarly journals Heat Transfer Model of Multilayer Thermal Protective Clothing for High-Temperature Operation

2019 ◽  
Vol 13 (11) ◽  
pp. 21
Author(s):  
Man-Jing Li ◽  
Mao Zhu ◽  
Jia-Xu Han ◽  
Yuan-Biao Zhang
Keyword(s):  
Heat Transfer ◽  
Heat Conduction ◽  
Steady State ◽  
High Temperature ◽  
Protective Clothing ◽  
Heat Transfer Model ◽  
Heat Radiation ◽  
Transfer Model ◽  
Thermal Protective Clothing ◽  
High Temperature Operation

The thermal protective clothing for high-temperature operation usually consists of three-layer fabrics and a gap called the air layer or Layer IV between Layer III and skin. In order to design more effective thermal protective clothing at less cost, based on the heat transfer principles, we establish heat transfer models of fabrics and air layer, which are one-dimensional nonlinear partial differential equations with constant coefficients. In the three-layer fabrics, we consider the effects of heat conduction and heat radiation in Layer I but only consider heat conduction in Layer II and Layer III. Furthermore, the heat transfer model of Layer IV is decoupled and simplified to steady-state heat conduction in Layer IV and radiation heat transfer on surface of Layer IV. According to the explicit difference schemes for the models, we use the parameters in an experiment which puts a thermal manikin in high-temperature environment for some time and measures the temperature of lateral skin at regular time, to solve the models and calculate the temperature of each layer. With MATLAB, the visual interface of three-dimensional temperature distribution is provided, which is reference for functional design of thermal protective clothing. We also compare the simulation result of skin surface with the experimental data. The results show that at the same position, the temperature rises over time but with decreasing rate and finally reaches the steady state. Moreover, at one moment after reaching the steady state, the temperature has a gradual decrease with the increase of distance from the external environment.

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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