Neural networks and stochastic search methods applied to industrial capacitive tomography

Control Engineering Practice

Volumn 7, Issue 1, 1999, Pages 117-121

  Tapson, J ^a  

^a UNIVERSITY OF CAPE TOWN   (South Africa)

Author keywords

Capacitive tomography; Classification; Genetic algorithms; Neural networks; Process tomography; Stochastic searching

Indexed keywords

GENETIC ALGORITHMS; NEURAL NETWORKS; SENSORS; STOCHASTIC CONTROL SYSTEMS; TOMOGRAPHY;

CAPACITIVE TOMOGRAPHY;

PROCESS CONTROL;

INDUSTRIAL APPLICATION; NEURAL NETWORK; SENSOR; SIGNAL PROCESSING; TOMOGRAPHY;

EID: 0032698097     PISSN: 09670661     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0967-0661(98)00139-7     Document Type: Article

References (5)

1. Baluja, S. (1994). Population-based incremental learning: a method for integrated genetic search based function optimisation and competitive learning. Technical Report CMU-CS-94-163, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
2. Beck, M.S., & Williams, R.A., (1996). Process tomography: a European innovation and its applications. Meas. Sci. Technol., 7, 215-224.
3. Marioli, D., Sardini, E. & Taroni, A. (1993). High accuracy measurement techniques for capacitance transducers. Meas. Sci. Technol., 4, 337-343.
4. Masters, T. (1992). Neural network recipes in C++ , New York: Academic Press.
5. Stott, A.L., Green, R.G., & Seraji, K. (1985). Comparison of the use of internal and external electrodes for the measurement of the capacitance and conductance of fluids in pipes. J. Phys. E, 18, 587-592.