- Témaindító
- #1
- Csatlakozás
- 2023.06.08.
- Üzenetek
- 25,214
- Reakció pontszám
- 177
- Díjak
- 6
- Kor
- 36
| 208 | Ankur Kumar |
This book provides a comprehensive coverage of ML methods that have proven useful in process industry for dynamic process modeling. Step-by-step instructions, supported with industry-relevant case studies, show how to develop solutions for process modeling, process monitoring, etc., using classical and modern methods
This book has been divided into three parts. Part 1 of the book provides perspectives on the importance of ML for dynamic process modeling and lays down the basic foundations of ML-DPM (machine learning for dynamic process modeling). Part 2 provides in-detail presentation of classical ML techniques (such as ARX, FIR, OE, ARMAX, ARIMAX, CVA, NARX, etc.) and has been written keeping in mind the different modeling requirements and process characteristics that determine a model's suitability for a problem at hand. These include, amongst others, presence of multiple correlated outputs, process nonlinearity, need for low model bias, need to model disturbance signal accurately, etc. Part 3 is focused on artificial neural networks and deep learning.
The following topics are broadly covered
· Exploratory analysis of dynamic dataset
· Best practices for dynamic modeling
· Linear and discrete-time classical parametric and non-parametric models
· State-space models for MIMO systems
· Nonlinear system identification and closed-loop identification
· Neural networks-based dynamic process modeling
Contents of Download:
Machine Learning in Python for Dynamic Process Systems.pdf (10.23 MB)
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