By Dr. Shankar Narasimhan Ph.D. (Ch.E.)
ISBN10: 0884152553
ISBN13: 9780884152552

This is a superb ebook at the topic  the authors have lined the entire bases. if you would like a ebook on info reconciliation and gross blunders detection, this is often as entire and thorough a ebook as i will think.  Les A. Kane, Editor, complicated procedure keep an eye on and data platforms
Content:
Acknowledgments
, Pages xiiixiv
Preface
, Pages xvxvii
1  the significance of information Reconciliation and Gross blunders Detection
, Pages 131
2  size mistakes and blunder aid Techniques
, Pages 3258
3  Linear SteadyState info Reconciliation
, Pages 5984
4  SteadyState info Reconciliation for Bilinear Systems
, Pages 85118
5  Nonlinear SteadyState information Reconciliation
, Pages 119141
6  facts Reconciliation in Dynamic Systems
, Pages 142173
7  creation to Gross mistakes Detection
, Pages 174225
8  a number of Gross errors identity suggestions for SteadyState Processes
, Pages 226280
9  Gross blunders Detection in Linear Dynamic Systems
, Pages 281299
10  layout of Sensor Networks
, Pages 300326
11  commercial functions of knowledge Reconciliation and Gross errors Detection Technologies
, Pages 327372
Appendix A  easy thoughts in Linear Algebra
, Pages 373377
Appendix B  Graph thought Fundamentals
, Pages 378383
Appendix C  basics of chance and Statistics
, Pages 384393
Index
, Pages 394402
Author Index
, Pages 403405
The Authors
, Page 406
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Extra resources for Data Reconciliation and Gross Error Detection. An Intelligent Use of Process Data
Example text
The former estimates the current value based on the current and past measurements and it is of primary concern in process control. The latter estimates the value of the central point from past and recent measurements (values from both sides of the central point) and it is mainly used for fault diagnosis and steadystate process optimization. Many authors, however, do not distinguish between the two terms and use the data "smoothing" term for data filtering as well. An integral of absolute errors (IAE) similar to that of Kim and Lee [10] will be used to compare various filtering techniques in this text.
The E W M A filter is analytically described as [3]: (224) Yk  ~Yk + (1  ~) Yk1 where Yk = sample mean (moving average with equal weights) at time tk Yk = filtered value at time tk ~, = filter parameter ; 0 < ~, < 1 Initially, Y0 is taken as the control target kt0 ( Y0 = kt0). to k  1 , 2 , 3 ..... (225) j=O Equation 225 indicates that the weights assigned to the sample means decrease geometrically with age. For that reason, this filter is sometimes referred to as the geometric moving average filter [3].
Is there any incentive to use the more complex filter weights given by Equation 223 rather than a simple moving average filter with equal weights ? Note that the filter with unequal exponential weights described above by Equation 222 and 223 is to be distinguished from the exponentially weighted moving average (EWMA)filter which is often used in statistical process control area. The E W M A filter is analytically described as [3]: (224) Yk  ~Yk + (1  ~) Yk1 where Yk = sample mean (moving average with equal weights) at time tk Yk = filtered value at time tk ~, = filter parameter ; 0 < ~, < 1 Initially, Y0 is taken as the control target kt0 ( Y0 = kt0).
Data Reconciliation and Gross Error Detection. An Intelligent Use of Process Data by Dr. Shankar Narasimhan Ph.D. (Ch.E.)
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