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 |
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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 |
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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