Garcia-Gimeno, R.M., Hervas-Martinez, C. and de Siloniz, M.I. (2002) Improving Artificial Neural Networks with a Pruning Methodology and Genetic Algorithms for Their Application in Microbial Growth Prediction in Food. International Journal of Food Microbiology, 72, 19-30.
http://dx.doi.org/10.1016/S0168-1605(01)00608-0

has been cited by the following article:

• TITLE: Modeling the Surface Ozone Concentration in Campo Grande (MS)—Brazil Using Neural Networks

  AUTHORS: Amaury de Souza, Flavio Aristone, Ismail Sabbah

  KEYWORDS: Neural Networks, Climate, Surface Ozone, Public Health, Air Quality

  JOURNAL NAME: Natural Science, Vol.7 No.4, April 13, 2015

  ABSTRACT: The estimation of the surface ozone concentration promotes the creation of data useful for planning the air quality forecast, which is a key element for the management of public health. The aim of this study is to develop an Artificial Neural Network (ANN) to estimate the concentration of surface ozone from daily climate data. ANN is an equivalent form of Feedforward Multilayer Perceptron whose data has been inserted from the daily concentration of measured ozone. In the intermediate and output layers activation functions like tan-sigmoid and linear have been used, respectively. The performance of the developed ANN is actually very good and it can be considered like part of the set of indirect methods to estimate the concentration of surface ozone. The proposed model may be used by governmental agencies as a tool to enable the public interventional actions during the period of atmospheric stagnation, when ozone levels in the atmosphere represent risks to the public health.