Fuzzy self-organizing hybrid neural network for gas analysis system
Stanisław Osowski , Kazimierz Brudzewski
AbstractThe paper presents the gas analysis system applying the self-organizing fuzzy hybrid neural network. The network is composed of the self-organizing competitive fuzzy layer and the supervised multilayer perceptron (MLP) subnetwork, connected in cascade. The characteristic features of this network structure for gas analysis systems are discussed and the results of experiments compared to standard neural solutions based on MLP or classical hybrid network employing the Kohonen layer
|Journal series||IEEE Transactions On Instrumentation And Measurement, ISSN 0018-9456|
|Keywords in English||air pollution measurement, array signal processing, cascade connected, chemical engineering computing, feature extraction, feedforward neural nets, fuzzy neural nets, fuzzy self-organizing hybrid neural network, gas analysis system, gas pollutants recognition, gas sensors, learning (artificial intelligence), learning patterns, mean absolute error, multilayer perceptrons, pattern clustering, self-organising feature maps, self-organizing competitive fuzzy layer, semiconductor oxide gas sensors, Sensor array, signal processing, supervised multilayer perceptron subnetwork|
|Publication indicators||= 7; = 5; = 12.0; : 2000 = 0.861; : 2006 = 0.572 (2) - 2007=0.952 (5)|
|Citation count*||12 (2016-07-08)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.