Research on Data Processing Method of High Altitude Meteorological Parameters Based on Neural Network

The high altitude meteorological parameters include longitude, latitude, atmospheric pressure, temperature, humidity, etc., which are influenced between each parameter, therefore, it is very important to deal with and analyze these parameters. In this paper, we employ MATLAB software, research the basic algorithm of BP neural network, compare resilient BP algorithm, the Fletcher- Reeves algorithm and the proportion conjugate gradient algorithm, establish the forecast model of high altitude meteorological data with Fletcher-Reeves algorithm, and analyze the influence of the hidden layer nodes of training. Finally, we analyze the errors of the sensor models, obtain the optimal results using neural network, and ascertain the final data processing method.

[1]  Odej Kao,et al.  Exploiting Dynamic Resource Allocation for Efficient Parallel Data Processing in the Cloud , 2011, IEEE Transactions on Parallel and Distributed Systems.

[2]  Jun Wang,et al.  A general projection neural network for solving monotone variational inequalities and related optimization problems , 2004, IEEE Transactions on Neural Networks.

[3]  Narayanan Kumarappan,et al.  Day-Ahead Deregulated Electricity Market Price Forecasting Using Recurrent Neural Network , 2013, IEEE Systems Journal.

[4]  I. Fernandez,et al.  Characterization of Maximum Radar Reflectivity Height During Stratiform Rain Events , 2012, IEEE Transactions on Antennas and Propagation.

[5]  Yunbo Shi,et al.  Research of high altitude meteorological data processing method , 2012, Proceedings of 2012 International Conference on Measurement, Information and Control.

[6]  Menghua Wang,et al.  Near-Real-Time Ocean Color Data Processing Using Ancillary Data From the Global Forecast System Model , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Peijun Shi,et al.  Spatial downscaling of TRMM precipitation data based on the orographical effect and meteorological conditions in a mountainous area , 2013 .

[8]  Neri Merhav,et al.  Data Processing Theorems and the Second Law of Thermodynamics , 2010, IEEE Transactions on Information Theory.

[9]  Peter Reinartz,et al.  Alphabet-Based Multisensory Data Fusion and Classification Using Factor Graphs , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.