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A maximum entropy (MaxEnt) based probabilistic approach is developed to model mechanical systems characterized by symmetric positive-definite matrices bounded from below and above. These matrices are typically encountered in the constitutive modeling of heterogeneous materials, where the bounds are deduced by employing the principles of minimum… (More)

The Fisher information matrix (FIM) is a critical quantity in several aspects of mathematical modeling, including input selection, model selection, and confidence region calculation. For example, the determinant of the FIM is the main performance metric for choosing input values in a scientific experiment with the aims of achieving the most accurate… (More)

- Sonjoy Das
- 2008

- Sonjoy Das
- 2007

The Fisher information matrix (FIM) is a critical quantity in several aspects of mathematical modeling, including input selection and confidence region calculation. Analytical determination of the FIM in a general setting, specially in nonlinear models , may be difficult or almost impossible due to intractable modeling requirements and/or intractable… (More)

— The Fisher information matrix (FIM) is a critical quantity in several aspects of system identification, including input selection and confidence region calculation. Analytical determination of the FIM in a general system identification setting may be difficult or almost impossible due to intractable modeling requirements and/or high-dimensional… (More)

— In this paper, we generalize our random matrix based (RM-based) uncertainty model for manipulator Jacobian matrix to the dynamic model of the robotic systems. Conventional random variable based (RV-based) schemes require a detailed knowledge of the system parameters variation and may be not able to fully characterize the uncertainties of the complex… (More)

In this paper, we formulate the manipulator Jacobian matrix in a probabilistic framework based on the random matrix theory (RMT). Due to the limited available information on the system fluctuations, the parametric approaches often prove to be inadequate to appropriately characterize the uncertainty. To overcome this difficulty, we develop two RMT-based… (More)

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