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008 080827t2008 my a f m 000 0 eng|d
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040 _aUMP
090 _aQA76.87 .S53 2008 rs Thesis
100 0 _aShankar Ramakishan
245 1 0 _aDevelopment of inferential measurement for air density using neural network /
_cShankar Ramakishan
246 3 _aDevelopment of inferential measurement for air density using neural network /
_h[electronic resource]
260 _aKuantan, Pahang :
_bUMP,
_c2008
300 _a68 p. :
_bill. (some col.) ;
_c30 cm. +
_e1 computer disc
502 _aProject paper (Bachelor of Chemical Engineering) --- Universiti Malaysia Pahang - 2008
520 3 _aIn many industrial processes, the most desirable variables to control are measured infrequently off-line in a quality control laboratory. In these situations, use of advanced control or optimization techniques requires use of inferred measurements generated from correlations. For well-understood processes, the structure of the correlation as well as the choice of inputs may be known a priori. However, many industrial processes are too complex and the appropriate form of the correlation and choice of input measurements are not obvious. Here, process knowledge, operating experience, and statistical methods play an important role in development of correlations. This paper describes a systematic approach to the development of nonlinear correlations for inferential measurements using neural networks. A three-step procedure is proposed. The first step consists of data collection and preprocessing. Next, the process variables are subjected to simple statistical analyses to identify a subset of measurements to be used in the inferential scheme. The third step involves generation of the inferential scheme. -Author
650 0 _aNeural networks (Computer science)
999 _aVIRTUA40
_c2645
_d2651
999 _aVTLSSORT0080*0200*0400*0900*1000*2450*2460*2600*3000*5020*5200*6500*9992