Name
Abstract | ||
|---|---|---|
The capability to solve ordinary differential equations (ODE) in hardware will increase the operation capacity of sensing systems in areas such as self-diagnostics, model-based measurement and self-calibration. The computational complexity of solving ODE must be reduced in order to implement a real-time embedded ODE solver. The research proposes a novel design that proves the possibility of solving ODE in real-time embedded systems with reasonably high degree of precision and efficiency. The application of three approximation methods namely, multi-layer perceptron, radial basis network and Lipschitz continuous interpolation is researched and compared. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1504/IJISTA.2009.025104 | IJISTA |
Keywords | Field | DocType |
operation capacity,high degree,embedded differential equation solver,model-based measurement,real-time embedded ode solver,novel design,multi-layer perceptron,approximation method,lipschitz continuous interpolation,computational complexity,real-time embedded system,function approximation,ode,differential equation,ordinary differential equations,sensors,neural networks,embedded systems,state observer | Differential equation,Applied mathematics,Ordinary differential equation,Function approximation,Interpolation,Algorithm,Control engineering,Lipschitz continuity,Solver,Ode,Mathematics,Computational complexity theory | Journal |
Volume | Issue | Citations |
7 | 1 | 0 |
PageRank | References | Authors |
0.34 | 3 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ye Chow Kuang | 1 | 72 | 19.81 |
| Melanie Po-Leen Ooi | 2 | 70 | 18.35 |
| Wei Hong Loh | 3 | 0 | 0.34 |
| Serge Demidenko | 4 | 47 | 7.78 |