Download H06A5a| Bioresponse measurements and process

|H06A5a| Bioresponse measurements and process control
Chapter 5: Introduction in the modelling of bioresponses.
1. Definitions and types of models
1.1. Definitions
1.2. Classifications of models
2. Overview of modelling bioresponses
2.1. Overview of literature models of responses of animals and human beings (19612000)
2.2. Choice of the type of model
Chapter 6: Generation of dynamic data for the modelling of bioresponsens
1. Data based models
1.1. When use data based model? Are they applicable for cyclists?
1.2. Different types of dynamic data based models
- Transfer function models
- State space models
- Neural networks
1.3. The identification loop
2. Setting of dynamic experiments
2.1. Importance of good experiments
2.2. Choice of the variables
2.3. Choice of the measured interval
2.4. Choice of the input signals
2.5. Pretreatment of the measured signals
- High frequency disturbances
- Outliers
- Drift, low frequency disturbances
Chapter 7: Offline parameter estimation to model bioresponses
1. Transfer function models
2. Least squares method
2.1. Linear regression
2.2. Principle of least squares
2.3. Geometric interpretation
2.4. Limits of least squares
2.5. Least squares and transfer function models
2.6. ARX transfer function models
3. Pseudo-linear regression models
3.1. Output error transfer function model (OE)
4. Instrumental variable method (IV)
5. Refined instrumental variable method (RIV)
6. Comparison of least squares and SRIV
Chapter 8: Determination of model structure and validation
1. determination of model structure
1.1. Criteria for identification of system order
- R² value
- AIC
- YIC
1.2. Example order determination
2. Validation of models
2.1. Definition of model validation
2.2. Methods validation
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Checking the poles and zeroes
Checking the frequency response
Confidence intervals of parameters
Simulations
Testing model errors
o ‘Whiteness test’ or auto-correlation test
o Cross correlation test
Chapter 9: Biological interpretation and higher order models
1. Higher order models
1.1. Serial coupling
1.2. Parallel coupling
1.3. Feedback coupling
1.4. Determination of time constants of subprocesses
- First order systems in series and parallel
- First order systems in feedback
1.5. Examples of higher order bioresponses
2. Biological interpretation
2.1. Choosing a model structure based on biological knowledge
2.2. Combining a data based model with a mechanistic model
- Mechanistic model of heat transfer
- Data based model of heat transfer
- Coupling of mechanistic with data based model
- Experiments
- Results
Chapter 10: Online parameter estimation
1. Time invariant systems: definition
2. Biological processes
2.1. Biological processes are time variant
2.2. Biological processes are non-linear
2.3. Other needs for adaptive estimation
3. Recursive parameter estimation
3.1. Introduction: non-recursive parameter estimation
3.2. Recursive parameter estimation
4. Example: growth modelling of broilers
Chapter 11: Model based control and monitoring
1. MPC controllers
1.1. Introduction
1.2. Definitions of MBPC
1.3. Model of the system
1.4. Cost functions
1.5. Determination of control input
1.6. Numerical example
1.7. Comparison PID&MBPC
2. Examples of MPC controllers
2.1. MPC for ventilation steering
2.2. Controlling growth of broiler chickens online based on a compact predictive growth
model
- Introduction
- Objectives
- Materials and methods
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o Feedback of animal weight
o Growth modelling
o Growth control
o Experiments
- Results
- Conclusions
2.3. Design of a demonstration set-up for the active steering of crawl trajectories of fly
larvae
- Introduction
- Objective
- Literature
- Pre-experiments
- Set-up
- Results
- Conclusions
2.4. Design of a controller for heart beat based steering for racing cyclists
3. Model based monitoring
3.1. Model based monitoring
3.2. Biomonitor-daphnia monitor
3.3. Plant stress monitoring
- Online modelling
- Cold stress during morning
- Increased air temperature
3.4. Monitoring of critically ill patients
- Data sets
- Monitoring
- Results
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