Tropospheric Ozone Column Retrieval From ESA-Envisat SCIAMACHY Nadir UV/VIS Radiance Measurements by Means of a Neural Network Algorithm

2012 ◽  
Vol 50 (3) ◽  
pp. 998-1011 ◽  
Author(s):  
Pasquale Sellitto ◽  
Fabio Del Frate ◽  
Domenico Solimini ◽  
Stefano Casadio
Keyword(s):  
Neural Network ◽  
Tropospheric Ozone ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Ozone Column

scholarly journals Tropospheric ozone column retrieval at northern mid-latitudes from the Ozone Monitoring Instrument by means of a neural network algorithm

2011 ◽  
Vol 4 (11) ◽  
pp. 2375-2388 ◽  
Author(s):  
P. Sellitto ◽  
B. R. Bojkov ◽  
X. Liu ◽  
K. Chance ◽  
F. Del Frate
Keyword(s):  
Neural Network ◽  
Test Data ◽  
Tropospheric Ozone ◽  
Optimal Estimation ◽  
Ozone Monitoring Instrument ◽  
Data Set ◽  
Monitoring Instrument ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Ozone Monitoring

Abstract. Monitoring tropospheric ozone from space is of critical importance in order to gain more thorough knowledge on phenomena affecting air quality and the greenhouse effect. Deriving information on tropospheric ozone from UV/VIS nadir satellite spectrometers is difficult owing to the weak sensitivity of the measured radiance spectra to variations of ozone in the troposphere. Here we propose an alternative method of analysis to retrieve tropospheric ozone columns from Ozone Monitoring Instrument radiances by means of a neural network algorithm. An extended set of ozone sonde measurements at northern mid-latitudes for the years 2004–2008 has been considered as the training and test data set. The design of the algorithm is extensively discussed. Our retrievals are compared to both tropospheric ozone residuals and optimal estimation retrievals over a similar independent test data set. Results show that our algorithm has comparable accuracy with respect to both correlative methods and its performance is slightly better over a subset containing only European ozone sonde stations. Possible sources of errors are analyzed. Finally, the capabilities of our algorithm to derive information on boundary layer ozone are studied and the results critically discussed.

A NEURAL NETWORK ALGORITHM FOR DENSITY MEASUREMENT OF MULTIPHASE FLOW

2012 ◽  
Vol 24 (2) ◽  
pp. 89-103 ◽  
Author(s):  
Nabeel Al-Rawahi ◽  
Mahmoud Meribout ◽  
Ahmed Al-Naamany ◽  
Ali Al-Bimani ◽  
Adel Meribout
Keyword(s):  
Neural Network ◽  
Multiphase Flow ◽  
Density Measurement ◽  
Network Algorithm ◽  
Neural Network Algorithm

Logistics demand forecasting model based on improved neural network algorithm

2020 ◽  
pp. 1-11
Author(s):  
Hongjiang Ma ◽  
Xu Luo
Keyword(s):  
Neural Network ◽  
Influencing Factor ◽  
Demand Forecasting ◽  
Forecasting Model ◽  
Logistics System ◽  
Model Based ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Demand Forecasting Model ◽  
Logistics Demand

The irrationality between the procurement and distribution of the logistics system increases unnecessary circulation links and greatly reduces logistics efficiency, which not only causes a waste of transportation resources, but also increases logistics costs. In order to improve the operation efficiency of the logistics system, based on the improved neural network algorithm, this paper combines the logistic regression algorithm to construct a logistics demand forecasting model based on the improved neural network algorithm. Moreover, according to the characteristics of the complexity of the data in the data mining task itself, this article optimizes the ladder network structure, and combines its supervisory decision-making part with the shallow network to make the model more suitable for logistics demand forecasting. In addition, this paper analyzes the performance of the model based on examples and uses the grey relational analysis method to give the degree of correlation between each influencing factor and logistics demand. The research results show that the model constructed in this paper is reasonable and can be analyzed from a practical perspective.

A fuzzy logic neural network algorithm for compressor crankshaft-rolling bearing system optimization

2021 ◽  
pp. 1-7
Author(s):  
Zheng Zhang ◽  
Jianrong Zheng
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Dynamic Analysis ◽  
Weighted Average ◽  
Rolling Bearing ◽  
System Optimization ◽  
Simulation Software ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Bearing System

Taking the crankshaft-rolling bearing system in a certain type of compressor as the research objective, dynamic analysis software is used to conduct detailed dynamic analysis and optimal design under the rated power of the compressor. Using Hertz mathematical formula and the analysis method of the superstatic orientation problem, the relationship expression between the bearing force and deformation of the rolling bearing is solved, and the dynamic analysis model of the elastic crankshaft-rolling bearing system is constructed in the simulation software ADAMS. The weighted average amplitude of the center of the neck between the main bearings is used as the target, and the center line of the compressor cylinder is selected as the design variable. Finally, an example analysis shows that by introducing the fuzzy logic neural network algorithm into the compressor crankshaft-rolling bearing system design, the optimal solution between the design variables and the objective function can be obtained, which is of great significance to the subsequent compressor dynamic design.

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