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Practical Grey-box Process Identification
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eBay item number:389055750299
Item specifics
- Condition
- Brand New: A new, unread, unused book in perfect condition with no missing or damaged pages. See all condition definitionsopens in a new window or tab
- ISBN-13
- 9781849965989
- Book Title
- Practical Grey-box Process Identification
- ISBN
- 9781849965989
About this product
Product Identifiers
Publisher
Springer London, The Limited
ISBN-10
1849965986
ISBN-13
9781849965989
eBay Product ID (ePID)
109169673
Product Key Features
Number of Pages
Xx, 351 Pages
Language
English
Publication Name
Practical Grey-Box Process Identification : Theory and Applications
Publication Year
2010
Subject
Computer Simulation, General, Manufacturing, Electrical, Applied
Type
Textbook
Subject Area
Mathematics, Computers, Technology & Engineering
Series
Advances in Industrial Control Ser.
Format
Trade Paperback
Dimensions
Item Weight
20.3 Oz
Item Length
9.3 in
Item Width
6.1 in
Additional Product Features
Intended Audience
Scholarly & Professional
Dewey Edition
22
Number of Volumes
1 vol.
Illustrated
Yes
Dewey Decimal
670.427
Table Of Content
Theory of Grey-box Process Identification.- Prospects and Problems.- The MoCaVa Solution.- Tutorial on MoCaVa.- Preparations.- Calibration.- Some Modelling Support.- Case Studies.- Rinsing of the Steel Strip in a Rolling Mill.- Quality Prediction in a Cardboard Making Process.
Synopsis
In process modelling, knowledge of the process under consideration is typically partial with significant unknown inputs (disturbances) to the model. Disturbances militate against the desirable trait of model reproducibility. "Grey-box" identification can assist, in these circumstances, by taking advantage of the two sources of information that may be available: any invariant prior knowledge and response data from experiments. Practical Grey-box Process Identification is a three-stranded response to the following questions which are frequently raised in connection with grey-box methods: How much of my prior knowledge is useful and even correct in this environment? Are my experimental data sufficient and relevant? What do I do about the disturbances that I can't get rid of? How do I know when my model is good enough? The first part of the book is a short review of the theoretical fundamentals of grey-box identification, focussing particularly on the theory necessary for the software presented in the second part. Part II puts the spotlight on MoCaVa, a MATLAB®-compatible software tool, downloadable from springeronline.com, for facilitating the procedure of effective grey-box identification. Part III demonstrates the application of MoCaVa using two case studies drawn from the paper and steel industries. More advanced theory is laid out in an appendix and the MoCaVa source code enables readers to expand on its capabilities to their own ends. Practical Grey-box Process Identification will be of great interest and help to process control engineers and researchers and the software show-cased here will be of much practical assistance to students doing project work in this field., In process modelling, knowledge of the process under consideration is typically partial with significant unknown inputs (disturbances) to the model. Disturbances militate against the desirable trait of model reproducibility. "Grey-box" identification can assist, in these circumstances, by taking advantage of the two sources of information that may be available: any invariant prior knowledge and response data from experiments. Practical Grey-box Process Identification is a three-stranded response to the following questions which are frequently raised in connection with grey-box methods: How much of my prior knowledge is useful and even correct in this environment? Are my experimental data sufficient and relevant? What do I do about the disturbances that I can't get rid of? How do I know when my model is good enough? The first part of the book is a short review of the theoretical fundamentals of grey-box identification, focussing particularly on the theory necessary for the software presented in the second part. Part II puts the spotlight on MoCaVa , a MATLAB(R)-compatible software tool, downloadable from springeronline.com, for facilitating the procedure of effective grey-box identification. Part III demonstrates the application of MoCaVa using two case studies drawn from the paper and steel industries. More advanced theory is laid out in an appendix and the MoCaVa source code enables readers to expand on its capabilities to their own ends. Practical Grey-box Process Identification will be of great interest and help to process control engineers and researchers and the software show-cased here will be of much practical assistance to students doing project work in this field., In process modelling, knowledge of the process under consideration is typically partial with significant disturbances to the model. Disturbances militate against the desirable trait of model reproducibility. "Grey-box" identification takes advantage of two sources of process information that may be available: any invariant prior knowledge and response data from experiments. "Practical Grey-box Process Identification" is in three parts: The first part is a short review of the theoretical fundamentals of grey-box identification, focussing particularly on the theory necessary for the software presented in the second part. Part II puts the spotlight on MoCaVa, a MATLAB(R)-compatible software tool, downloadable from springeronline.com, for facilitating the procedure of effective grey-box identification. Part III demonstrates the application of MoCaVa using two case studies drawn from the paper and steel industries. More advanced theory is laid out in an appendix and the MoCaVa source code enables readers to expand on its capabilities to their own ends., In process modelling, knowledge of the process under consideration is typically partial with significant disturbances to the model. Disturbances militate against the desirable trait of model reproducibility. "Grey-box" identification takes advantage of two sources of process information that may be available: any invariant prior knowledge and response data from experiments. "Practical Grey-box Process Identification" is in three parts: The first part is a short review of the theoretical fundamentals of grey-box identification, focussing particularly on the theory necessary for the software presented in the second part. Part II puts the spotlight on MoCaVa, a MATLAB®-compatible software tool, downloadable from springeronline.com, for facilitating the procedure of effective grey-box identification. Part III demonstrates the application of MoCaVa using two case studies drawn from the paper and steel industries. More advanced theory is laid out in an appendix and the MoCaVa source code enables readers to expand on its capabilities to their own ends.
LC Classification Number
TJ212-225
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Seller feedback (1,066,272)
- eBay 自動留下信用評價- Feedback left by buyer.Past month訂單準時送達，沒遇到任何問題
- s***i (646)- Feedback left by buyer.Past monthVerified purchaseGreat book fast shipping thank you
- eBay 自動留下信用評價- Feedback left by buyer.Past month訂單準時送達，沒遇到任何問題