Application Self-organizing Map Type in a Study of the Profile of Gasoline C Commercialized in the Eastern and Northern Parana Regions

Artificial neural networks self-organizing map type (SOM) was used to classify samples of automotive gasoline C marketed in the eastern and northern regions of the state of Paraná, Brazil. The input order of parameters in the network were the values of temperature of the first drop, the 10, 50 and 90% distilled bulk, the final boiling point, density, residue content and alcohol content. A network with a topology of 25x25 and 5000 training epochs was used. The weight maps of input parameters for the trained network identified that the most important parameters for classifying samples were the temperature of the first drop and the temperature of the 10% and 50% of the distilled fuel. DOI: http://dx.doi.org/10.17807/orbital.v7i2.73

Self-Organizing Maps Neural Networks Applied to the Classification of Ethanol Samples According to the Region of Commercialization

Physical-chemical analysis data were collected, from 998 ethanol samples of automotive ethanol commercialized in the northern, midwestern and eastern regions of the state of Paraná. The data presented self-organizing maps (SOM) neural networks, which classified them according to those regions. The self-organizing maps best configuration had a 45 x 45 topology and 5000 training epochs, with a final learning rate of 6.7x10-4, a final neighborhood relationship of 3x10-2 and a mean quantization error of 2x10-2. This neural network provided a topological map depicting three separated groups, each one corresponding to samples of a same region of commercialization. Four maps of weights, one for each parameter, were presented. The network established the pH was the most important variable for classification and electrical conductivity the least one. The self-organizing maps application allowed the segmentation of alcohol samples, therefore identifying them according to the region of commercialization. DOI: http://dx.doi.org/10.17807/orbital.v9i4.982</p

<b>The use of multilayer perceptron artificial neural networks for the classification of ethanol samples by commercialization region

Samples of automotive ethanol, marketed in the northern and eastern regions of the state of Paraná, Brazil, underwent physical and chemical tests. Rates were assessed by Multilayer Perceptron (MLP) neural network for classification. For network training, two hundred epochs, a 0.05 learning rate and a random subdivision of samples in three groups with 70 for training, 15 for test and 15% for validation were employed. Sixty networks were trained from three different initializations. Three networks, one at each start-up, were highlighted and the one with the best performance presented 8 neurons in the hidden layer, with 95 accuracy training, 96 in the test and 96% in validation. The most important variables in classifications, identified by the network, occurred in the following order: alcohol content, density, pH and electrical conductivity. Application of MLP segmented ethanol samples and identified the commercialization regions