Rapid Simulated Annealing Algorithm for optimization of Aeroengine Control Based on BP Neural Network

Process parameters of nanostructured ZrO2-7%Y2O3 coating during plasma spraying on the properties of the coating was optimized based on simulated annealing algorithm. BP neural network was applied to compute fitness of simulated annealing algorithm. A BP neural network model was built, four process parameters were input , the parameters included spraying distance, spraying electric current, primary gas pressure and secondary gas pressure, bonding strength of coating was output. Network was trained by orthogonal test data. Process parameters of coating were optimized by simulated annealing algorithm. The results show that maximal bonding strength of coating is 43.0377MPa. Process parameters for plasma spraying nanostructured ZrO2-7%Y2O3 coating are spraying distance of 80mm, spraying electric current of 977.0283A, primary gas pressure of 0.3046MPa and secondary gas pressure 0.9886MPa.

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BP Neural Network Based on Simulated Annealing Algorithm Optimization for Financial Crisis Dynamic Early Warning Model

Computational Intelligence and Neuroscience ◽

10.1155/2021/4034903 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Ying Chen

Keyword(s):

Neural Network ◽

Logistic Regression ◽

Simulated Annealing ◽

Early Warning ◽

Bp Neural Network ◽

Simulated Annealing Algorithm ◽

Model Accuracy ◽

Early Warning Model ◽

Annealing Algorithm ◽

Warning Model

Financial early warning mechanism is of great significance to the long-term healthy development and stable operation of listed enterprises. This paper adopts the logistic regression early warning model and BP neural network early warning model. Based on the BP neural network t early warning model optimized by the simulated annealing algorithm, the prediction effects of the model are compared from the perspectives of model accuracy and variable importance. Through the comparative analysis of the empirical results of the three methods, it can be seen that the simulated annealing algorithm has many advantages. The combination of the simulated annealing algorithm with multithreading, data compression, and segmentation greatly improves the efficiency of the algorithm and shortens the running time. Using the logistic regression early warning model and BP neural network early warning model and based on the BP neural network t early warning model optimized by the simulated annealing algorithm, the prediction effects of the model are compared from the perspective of model accuracy and variable importance. The results show that the three index dimensions of the BP neural network optimized by the simulated annealing algorithm have good discrimination ability to financial status. The BP neural network early warning model optimized based on the simulated annealing algorithm has good prediction accuracy and good practical significance.

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A New Hybrid Framework Based on Improved Genetic Algorithm and Simulated Annealing Algorithm for Optimization of Network IDS Based on BP Neural Network

Innovations in Smart Cities Applications Edition 2 - Lecture Notes in Intelligent Transportation and Infrastructure ◽

10.1007/978-3-030-11196-0_43 ◽

2019 ◽

pp. 507-521 ◽

Cited By ~ 1

Author(s):

Zouhair Chiba ◽

Noreddine Abghour ◽

Khalid Moussaid ◽

Amina El omri ◽

Mohamed Rida

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Simulated Annealing ◽

Bp Neural Network ◽

Simulated Annealing Algorithm ◽

Improved Genetic Algorithm ◽

Hybrid Framework ◽

Annealing Algorithm

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Prediction of rock mass parameters in the TBM tunnel based on BP neural network integrated simulated annealing algorithm

Tunnelling and Underground Space Technology ◽

10.1016/j.tust.2019.103103 ◽

2020 ◽

Vol 95 ◽

pp. 103103 ◽

Cited By ~ 7

Author(s):

B. Liu ◽

R. Wang ◽

G. Zhao ◽

X. Guo ◽

Y. Wang ◽

...

Keyword(s):

Neural Network ◽

Simulated Annealing ◽

Rock Mass ◽

Bp Neural Network ◽

Simulated Annealing Algorithm ◽

Annealing Algorithm

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Forecasting the Exchange Rate Based on BP Neural Network in Combination with Simulated Annealing Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.753-755.2930 ◽

2013 ◽

Vol 753-755 ◽

pp. 2930-2934

Author(s):

Li Meng

Keyword(s):

Neural Network ◽

Exchange Rate ◽

Simulated Annealing ◽

Bp Neural Network ◽

Prediction Accuracy ◽

Simulated Annealing Algorithm ◽

Linear Part ◽

Result Show ◽

Annealing Algorithm ◽

The Exchange Rate

In this study, the author focus on the exchange rate forecasting. Exchange rates fluctuation is extremely complex, not only contains the linear part but also includes non-linear elements, In this paper, Simulated Annealing Algorithm is introduced to overcome the neural network easy fall into local minimum defects in BP neural network basis, in order to optimize the network weights and thresholds, and thus improve the prediction accuracy. Through several forecast experiments about the major currencies against, the result show that compare to the single use of BP neural network, after introduced Simulated Annealing Algorithm, the prediction accuracy and stability has been further improved, meanwhile time-consuming less than genetic algorithms and other optimization algorithms.

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