Classificador de qualidade de álcool combustível e poder calorífico de gás GLP. / Alcohol combustible quality and LPG gas calorific power classifier.

AUTOR(ES)
DATA DE PUBLICAÇÃO

2004

RESUMO

This work shows the results of a robust system development as an alternative to recognize the quality of an alcohol fuel vapor sample and Liquid Petrol Gas (LPG) heat power in an electric nose. Two experimental methodologies were implemented to extract the features of alcohol fuel vapor and LPG gas patterns. The first approach to process the data used an Fuzzy Inference System (FIS) and two training algorithms of Artificial Neural Networks (ANN) to recognize alcohol fuel vapor patterns: Backpropagation and Learning Vector Quantization. The second approach consists of process data to develop an LPG heat power recognizing system robust to one-random-sensor-loss. Three systems were used. The first implemented an ANN to recognize all data that simulated the failure of a random sensor. This system had 97% of right responses. The second implemented seven ANN’s trained with input data subsets, such that six ANN’s were trained with a different failure sensor, and the seventh ANN was trained with data of all sensors without failure. This system had 99% of right responses. The third implemented an Ensemble Static Learning Machine containing ten parallel RNA’s to solve the problem. The result were 97% of right responses. RNA’s had better results than FIS. Some ways of hardware implementation of the recognizing system were suggested in DSP and micro-controllers pre-built systems.

ASSUNTO(S)

artificial neural networks fuzzy reconhecimento de padrões gas combustible (classification) redes neurais componentes principais (análise) principal component (analysis) fuzzy pattern recognition combustíveis gasosos (classificação)

ACESSO AO ARTIGO

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