Design of embedded differential equation solver

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.

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