Novel Framework Based on Genetic Algorithm and Simulated Annealing Algorithm for Optimization of BP Neural Network Applied to Network IDS

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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Damage Detection of Self-Anchored Suspension Bridge Based on Neural Network Model and Genetic-Simulated Annealing Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.243-249.1963 ◽

2011 ◽

Vol 243-249 ◽

pp. 1963-1967

Author(s):

Qing Chen Zhang ◽

Quan Sheng Sun

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Simulated Annealing ◽

Network Model ◽

Neural Network Model ◽

Simulated Annealing Algorithm ◽

Suspension Bridge ◽

Genetic Simulated Annealing Algorithm ◽

Annealing Algorithm ◽

Bp Neural Network Model

According to the characteristics of self-anchored suspension bridge, a new method to detect damage is introduced in this paper.It works in two stages.First, a BP neural network model is built to predict damaged position. Next, based on the characteristics of genetic algorithm and simulated annealing algorithm, a new approach, genetic-simulated annealing algorithm, is put forward to identify damage extent of detected positions. Compared with the traditional genetic algorithm, the global convergence effect of this algorithm is enhanced by using of the Metropolis acceptance rule of the simulated annealing algorithm in the searching process.

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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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