Title :
Evaluation and identification of lightning models by artificial neural networks
Author :
Silva, Ivan Nunes da ; De Souza, André Nunes ; Bordon, Mario Eduardo
Author_Institution :
Dept. of Electr. Eng., State Univ. of Sao Paulo, Brazil
Abstract :
This paper describes a novel approach for mapping lightning models using artificial neural networks. The networks acts as identifier of structural features of the lightning models so that output parameters can be estimated and generalised from an input parameter set. Simulation examples are presented to validate the proposed approach. More specifically, the neural networks are used to compute electrical field intensity and critical disruptive voltage taking into account several atmospheric and structural factors, such as pressure, temperature, humidity, distance between phases, height of bus bars, and wave forms. A comparative analysis with other approaches is also provided to illustrate this new methodology
Keywords :
feature extraction; feedforward neural nets; geophysics computing; lightning; parameter estimation; atmospheric factors; critical disruptive voltage; electrical field intensity; feature extraction; feedforward neural networks; lightning models; parameter estimation; Artificial neural networks; Atmospheric modeling; Atmospheric waves; Computational modeling; Computer networks; Humidity; Lightning; Parameter estimation; Temperature; Voltage;
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-5529-6
DOI :
10.1109/IJCNN.1999.830762