02173nam a2200241 a 4500 vtls000031227 KUKTEM 20251114204455.0 080827t2008 my a f m 000 0 eng|d THE0001922(Local) 201905131529 yusri 201107132240 VLOAD 200908141513 VLOAD 200908141443 VLOAD 200808271024 ida84 UMP QA76.87 .S53 2008 rs Thesis Shankar Ramakishan Development of inferential measurement for air density using neural network / Shankar Ramakishan Development of inferential measurement for air density using neural network / [electronic resource] Kuantan, Pahang : UMP, 2008 68 p. : ill. (some col.) ; 30 cm. + 1 computer disc Project paper (Bachelor of Chemical Engineering) --- Universiti Malaysia Pahang - 2008 In 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 Neural networks (Computer science) 0 0 0 1 20000 20000 2019-09-04 0 QA76.87 .S53 2008 rs Thesis 0000031554 2019-09-04 00:00:00 1 2019-09-04 PSM 0 0 0 1 10000 10000 2019-09-04 0 CD 2840 | QA76.87 .S53 2008 rs Thesis 0000031555 2019-09-04 00:00:00 1 2019-09-04 PSM VIRTUA40 2645 2651 VTLSSORT0080*0200*0400*0900*1000*2450*2460*2600*3000*5020*5200*6500*9992