Abstract
For continuous probability density functions (PDFs) approximated by the B-spline basis functions, the relationships between the B-spline weights, the entropy and the mean have been analyzed in detail. It shows the different characteristics of the entropy with and without mean constraint. A minimum entropy controller subjected to mean constraint is developed by taking the performance function as a Lyapunov function and ensuring the negativeness of its first-order derivative. Simulation examples are included to validate the analysis results and evaluate the closed-loop control performance.
| Original language | English |
|---|---|
| Pages | 1 - 6 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 2005 |
| Event | 16th IFAC World Congress, 2005 - Prague, Czech Republic Duration: 4 Jul 2005 → 8 Jul 2005 |
Conference
| Conference | 16th IFAC World Congress, 2005 |
|---|---|
| Country | Czech Republic |
| City | Prague |
| Period | 4/07/05 → 8/07/05 |
Keywords
- dynamic stochastic systems
- probability density function (PDF)
- B-spline neural network
- minimum entropy
- mean constraint
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Cite this
Zhou, J., Yue, H., & Wang, H. (2005). Mean and entropy of b-spline PDF models: analysis and design. 1 - 6. Paper presented at 16th IFAC World Congress, 2005, Prague, Czech Republic. https://doi.org/10.3182/20050703-6-CZ-1902.00368