MARC details
| 000 -LEADER |
|---|
| fixed length control field |
02173nam a2200241 a 4500 |
| 001 - CONTROL NUMBER |
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| control field |
vtls000031227 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
KUKTEM |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20251114204455.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
080827t2008 my a f m 000 0 eng|d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
THE0001922(Local) |
| 039 #9 - LEVEL OF BIBLIOGRAPHIC CONTROL AND CODING DETAIL [OBSOLETE] |
|---|
| Level of rules in bibliographic description |
201905131529 |
| Level of effort used to assign nonsubject heading access points |
yusri |
| Level of effort used to assign subject headings |
201107132240 |
| Level of effort used to assign classification |
VLOAD |
| Level of effort used to assign subject headings |
200908141513 |
| Level of effort used to assign classification |
VLOAD |
| Level of effort used to assign subject headings |
200908141443 |
| Level of effort used to assign classification |
VLOAD |
| -- |
200808271024 |
| -- |
ida84 |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
UMP |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
|---|
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
QA76.87 .S53 2008 rs Thesis |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Shankar Ramakishan |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Development of inferential measurement for air density using neural network / |
| Statement of responsibility, etc. |
Shankar Ramakishan |
| 246 3# - VARYING FORM OF TITLE |
|---|
| Title proper/short title |
Development of inferential measurement for air density using neural network / |
| Medium |
[electronic resource] |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
Kuantan, Pahang : |
| Name of publisher, distributor, etc. |
UMP, |
| Date of publication, distribution, etc. |
2008 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
68 p. : |
| Other physical details |
ill. (some col.) ; |
| Dimensions |
30 cm. + |
| Accompanying material |
1 computer disc |
| 502 ## - DISSERTATION NOTE |
|---|
| Dissertation note |
Project paper (Bachelor of Chemical Engineering) --- Universiti Malaysia Pahang - 2008 |
| 520 3# - SUMMARY, ETC. |
|---|
| Summary, etc. |
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 |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Neural networks (Computer science) |