Application of BP neural network optimized by genetic simulated annealing algorithm to prediction of air quality index in Lanzhou

2017 ◽  
Author(s):  
Zhou Kang ◽  
Zhiyi Qu
Keyword(s):  
Neural Network ◽  
Simulated Annealing ◽  
Air Quality ◽  
Bp Neural Network ◽  
Quality Index ◽  
Simulated Annealing Algorithm ◽  
Air Quality Index ◽  
Genetic Simulated Annealing Algorithm ◽  
Annealing Algorithm

scholarly journals An Unified Method of Exploration-Based Air Quality Prediction

2020 ◽  
Vol 8 (6) ◽  
pp. 4705-4708
Keyword(s):  
Neural Network ◽  
Air Quality ◽  
Bp Neural Network ◽  
Quality Index ◽  
Weather Condition ◽  
Air Quality Index ◽  
Air Quality Prediction ◽  
Large Current ◽  
New Information ◽  
Neural Network Algorithm

Data mining is the application of examining large current databases in sequence to create new information. It is a classification of artificial intelligence build on the concept that systems can get from data, analyze patterns and make judgment with minimal human intervention. The forecast of air quality is done with analyzing the AQI (Air Quality Index) of the atmosphere in different areas. These predictions are done using the BP Neural network Algorithm in which the data of the gases like CO2, CO, SO2, O3, NO2, PM2.5 etc. is first classified in the system, and then the normality is checked by comparison of each gases with the normality. But the prediction cannot be fully excepted because it doesn’t consider the outside weather condition of the atmosphere. This paper uses the ANN (Artificial Neural Network) technique along with BP Neural Network which analysis the weather condition of the atmosphere along with the data of the polluted gases. This paper predict more efficient air quality index of the atmosphere.

scholarly journals Hybrid Power Forecasting Model for Photovoltaic Plants Based on Neural Network with Air Quality Index

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Idris Khan ◽  
Honglu Zhu ◽  
Jianxi Yao ◽  
Danish Khan ◽  
Tahir Iqbal
Keyword(s):  
Neural Network ◽  
Air Quality ◽  
Bp Neural Network ◽  
Quality Index ◽  
Back Propagation ◽  
Air Quality Index ◽  
High Concentration ◽  
Photovoltaic Plants ◽  
Random Nature ◽  
Extra Parameter

High concentration of greenhouse gases in the atmosphere has increased dependency on photovoltaic (PV) power, but its random nature poses a challenge for system operators to precisely predict and forecast PV power. The conventional forecasting methods were accurate for clean weather. But when the PV plants worked under heavy haze, the radiation is negatively impacted and thus reducing PV power; therefore, to deal with haze weather, Air Quality Index (AQI) is introduced as a parameter to predict PV power. AQI, which is an indication of how polluted the air is, has been known to have a strong correlation with power generated by the PV panels. In this paper, a hybrid method based on the model of conventional back propagation (BP) neural network for clear weather and BP AQI model for haze weather is used to forecast PV power with conventional parameters like temperature, wind speed, humidity, solar radiation, and an extra parameter of AQI as input. The results show that the proposed method has less error under haze condition as compared to conventional model of neural network.

Process Parameters Optimization for Plasma Spraying Nanostructured ZrO2-7%Y2O3 Coating Based on Simulated Annealing Algorithm

2014 ◽  
Vol 1051 ◽  
pp. 12-16
Author(s):  
Bin Yang
Keyword(s):  
Neural Network ◽  
Simulated Annealing ◽  
Electric Current ◽  
Plasma Spraying ◽  
Process Parameters ◽  
Bp Neural Network ◽  
Bonding Strength ◽  
Simulated Annealing Algorithm ◽  
Gas Pressure ◽  
Annealing Algorithm

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.

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

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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