PID control as a process of active inference with linear generative models
- Submitting institution
-
University of Sussex
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 108674_83468
- Type
- D - Journal article
- DOI
-
10.3390/e21030257
- Title of journal
- Entropy
- Article number
- a257
- First page
- -
- Volume
- 21
- Issue
- 3
- ISSN
- 1099-4300
- Open access status
- Compliant
- Month of publication
- March
- Year of publication
- 2019
- URL
-
https://doi.org/10.3390/e21030257
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
1
- Research group(s)
-
-
- Citation count
- 3
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper demonstrates the deep connection between one of the most influential theories in the brain sciences, active inference, and a ubiquitous method in control theory, Proportional, Integral Derivative (PID) control. This paves the way for cross-fertilization between ideas in cognitive science and more traditional understanding of biological control. We also demonstrate a new methodology for the optimization of parameters in PID control theory.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -