Dynamic modeling of a polymeric composite interface: An introduction to in-situ neurocomputing in composite-based PH sensors

A. Talaie Osaka National Research Institute, Lab of Polymer Surface Chemistry, Midorigaoka, Ikeda, Osaka 563, Japan

J. Y. Lee Chemistry Department, University of New South Wales, Sydney, Australia

Y. K. Lee Physics Department, KyungHee University, Seoul, Korea

K. Adachi Osaka National Research Institute, Lab of Polymer Surface Chemistry, Midorigaoka, Ikeda, Osaka 563, Japan

T. Taguchi Osaka National Research Institute, Lab of Polymer Surface Chemistry, Midorigaoka, Ikeda, Osaka 563, Japan

E. Maeder Dresden Polymer research Institute, Dresden, Germany 5 Department of Intelligent Materials, Mechanical Engineering Department, Osaka City University, Osaka, Japan

J. Jang Physics Department, KyungHee University, Seoul, Korea

J. A. Romagnoli ICI Laboratory for Process System Engineering, Department of Chemical Engineering, University of Sydney, Sydney, Australia

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Abstract

A new computerized approach was considered for characterization and classification of different polymeric composites based pH sensors. In this approach an integrated artificial neural network (ANN)/composite was designed. Using this design we were able to create reproducible patterns and models based on the collected data. This new computerized method has advantages of being more selective and more accurate over conventional ways of data collection.

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Dynamic modeling of a polymeric composite interface: An introduction to in-situ neurocomputing in composite-based PH sensors

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Dynamic modeling of a polymeric composite interface: An introduction to in-situ neurocomputing in composite-based PH sensors

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