Structural Optimization of the Aircraft NACA Inlet Based on BP Neural Networks and Genetic Algorithms

Li, Zhimao; Chen, Changdong; Pei, Houju; Kong, Benben

doi:10.1155/2020/8857821

Cited by 7 publications

(5 citation statements)

References 8 publications

(7 reference statements)

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“…In the formula, A f is the formation abrasiveness coefficient, a 1 , a 2 is the rotational speed influence coefficient, which is determined by the bit type, Z 1 , Z 2 is the weight-on-bit influence coefficient, which is related to the diameter of the drill bit, and C 1 is the tooth wear slowdown coefficient [11][12][13].…”

Section: ) Objective Functionmentioning

confidence: 99%

Prediction and Optimization of Stability Parameters of Borehole Sensor for Deep Water Drilling Based on Genetic Algorithm

Huang¹,

Xia

Cai

et al. 2022

Journal of Sensors

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In order to study the prediction and optimization of borehole stability parameters in deepwater drilling based on genetic algorithm. First, a genetic hybrid algorithm based on pattern search is proposed. Then, based on the adaptive genetic algorithm, the evolutionary population is searched for patterns, which makes the hybrid algorithm not only has a strong global search ability but also improves the local optimization accuracy. Finally, the unit footage cost in the drilling process is taken as the objective function, and the algorithm is verified by taking the drilling in Karamay area as an example. The calculation results show that if the bit wear reaches 0.8-0.9 and then the bit is pulled out, the utilization rate of the bit can be increased, the design efficiency and accuracy can be improved, and the drilling cost can be reduced. The wear amount of the optimized bit is higher than that of the actual bit. Increasing the utilization rate of the bit can reduce the cost of drilling meters to a certain extent and improve the economic benefits of drilling. The objective function and constraint conditions for the optimization of drilling parameters are determined, and the algorithm is verified with the drilling data of Karamay Oilfield. The results show that the algorithm improves the stability and speed of iterative convergence and improves the reliability of data analysis results. Based on the regional three-dimensional formation rock parameter data volume, the optimization method can be used to optimize the drilling parameters before drilling and provide a basis for formulating the drilling design scheme.

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Section: ) Objective Functionmentioning

confidence: 99%

Prediction and Optimization of Stability Parameters of Borehole Sensor for Deep Water Drilling Based on Genetic Algorithm

Huang¹,

Xia

Cai

et al. 2022

Journal of Sensors

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“…When adjusting the weight of each layer of BP neural network, the error term is used to calculate and obtain the adjustment amount. When the operation result meets the end condition, the weight is automatically saved for evaluation and analysis [ 23 ]. If the calculation result does not meet the end condition, return the input vector again, and set the output vector until the end condition is met.…”

Section: Methodsmentioning

confidence: 99%

Evaluation Model of Innovation and Entrepreneurship Ability of Colleges and Universities Based on Improved BP Neural Network

2022

Computational Intelligence and Neuroscience

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Entrepreneurship education activities in colleges and universities play an important role in improving students’ innovation ability. Therefore, this paper has important practical value to evaluate the innovation and entrepreneurship ability of college students. At present, most studies use qualitative research methods, which is inefficient. Even if quantitative analysis is adopted, it is mostly linear analysis, which is inconsistent with the actual situation. In order to improve the application level of genetic algorithm to the innovation and entrepreneurship ability of universities based on BP neural network, this paper studies the evaluation model of innovation and entrepreneurship ability of universities. Based on the simple analysis of the current situation of university innovation and entrepreneurship ability evaluation and the application progress of BP neural network, combined with the actual situation of university innovation and entrepreneurship, this paper constructs the innovation and entrepreneurship evaluation index, uses BP neural network to build the evaluation model, and uses genetic algorithm to optimize and improve the shortcomings of BP neural network. Then, the experimental analysis and application design are carried out. The results show that the improved algorithm is basically consistent with the predicted value, small error, and fast convergence. When it is used in the evaluation of innovation and entrepreneurship ability, quantitative analysis results can be obtained, which provides a certain reference for the development of enterprises.

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“…All weights are assigned with random values initially. They are modified according to the learning samples traditionally by the delta rule [19][20][21][22].…”

Section: Hidden Layer Stagementioning

confidence: 99%

Application of Principal Component Analysis-Assisted Neural Networks for the Rotor Blade Load Prediction

Zheng

Jiao

Cui

2021

International Journal of Aerospace Engineering

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This paper presents a novel approach of principal component analysis- (PCA-) assisted back propagation (BP) neural networks for the problem of rotor blade load prediction. 86.5 hours of real flight data were collected from many steady-state and transient flight maneuvers at different altitudes and airspeeds. Prediction of the blade loads was determined by the PCA-BP model from 16 flight parameters measured and monitored by the flight control computer already present in the helicopter. PCA was applied to reduce the dimension of the flight parameters influencing the component load and eliminate the correlation among flight parameters. Thus, obtained principal components were used as input vectors of the BP neural network. The combined PCA-BP neural network model was trained and tested by real flight data. Comparison of this model and to a BP neural network model as well as to a multiple linear regression (MLR) model was also done. The results of comparison demonstrate that the PCA-BP model has higher prediction precision with an average error of 2.46%, while 4.49% for BP and 10.20% for MLR. The results also reveal that the PCA-BP model has a shorter convergence path than the BP model. This method not only is useful in establishing the load spectra of helicopter rotor in-service where installation of strain gauges is impractical but also can reduce the cost of installation and maintenance measured by strain gauges.

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Structural Optimization of the Aircraft NACA Inlet Based on BP Neural Networks and Genetic Algorithms

Cited by 7 publications

References 8 publications

Prediction and Optimization of Stability Parameters of Borehole Sensor for Deep Water Drilling Based on Genetic Algorithm

Prediction and Optimization of Stability Parameters of Borehole Sensor for Deep Water Drilling Based on Genetic Algorithm

Evaluation Model of Innovation and Entrepreneurship Ability of Colleges and Universities Based on Improved BP Neural Network

Application of Principal Component Analysis-Assisted Neural Networks for the Rotor Blade Load Prediction

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