Mathematical models for phenomena in the physical sciences are typically parameter-dependent, and the estimation of parameters that optimally model the trends suggested by experimental observation depends on how model...Mathematical models for phenomena in the physical sciences are typically parameter-dependent, and the estimation of parameters that optimally model the trends suggested by experimental observation depends on how model-observation discrepancies are quantified. Commonly used parameter estimation techniques based on least-squares minimization of the model-observation discrepancies assume that the discrepancies are quantified with the L<sup>2</sup>-norm applied to a discrepancy function. While techniques based on such an assumption work well for many applications, other applications are better suited for least-squared minimization approaches that are based on other norm or inner-product induced topologies. Motivated by an application in the material sciences, the new alternative least-squares approach is defined and an insightful analytical comparison with a baseline least-squares approach is provided.展开更多
The least squares(LS) minimization problem constitutes the core of many real-time signal processing problems. A square-root-free scaled Givens rotations algorithm and its systolic architecture for the optimal RLS resi...The least squares(LS) minimization problem constitutes the core of many real-time signal processing problems. A square-root-free scaled Givens rotations algorithm and its systolic architecture for the optimal RLS residual evaluation are presented in this paper. We analyze upper bounds of the dynamic range of processing cells and the internal parameters. Thus the wordlength can be obtained to prevent overflow and to ensure correct operations. Simulation results confirm the theoretical conclusions and the stability of the algorithm.展开更多
We address the problem of convergence of approximations obtained from two versions of the piecewise power-law representations arisen in Systems Biology. The most important cases of mean-square and uniform convergence ...We address the problem of convergence of approximations obtained from two versions of the piecewise power-law representations arisen in Systems Biology. The most important cases of mean-square and uniform convergence are studied in detail. Advantages and drawbacks of the representations as well as properties of both kinds of convergence are discussed. Numerical approximation algorithms related to piecewise power-law representations are described in Appendix.展开更多
文摘Mathematical models for phenomena in the physical sciences are typically parameter-dependent, and the estimation of parameters that optimally model the trends suggested by experimental observation depends on how model-observation discrepancies are quantified. Commonly used parameter estimation techniques based on least-squares minimization of the model-observation discrepancies assume that the discrepancies are quantified with the L<sup>2</sup>-norm applied to a discrepancy function. While techniques based on such an assumption work well for many applications, other applications are better suited for least-squared minimization approaches that are based on other norm or inner-product induced topologies. Motivated by an application in the material sciences, the new alternative least-squares approach is defined and an insightful analytical comparison with a baseline least-squares approach is provided.
文摘The least squares(LS) minimization problem constitutes the core of many real-time signal processing problems. A square-root-free scaled Givens rotations algorithm and its systolic architecture for the optimal RLS residual evaluation are presented in this paper. We analyze upper bounds of the dynamic range of processing cells and the internal parameters. Thus the wordlength can be obtained to prevent overflow and to ensure correct operations. Simulation results confirm the theoretical conclusions and the stability of the algorithm.
文摘We address the problem of convergence of approximations obtained from two versions of the piecewise power-law representations arisen in Systems Biology. The most important cases of mean-square and uniform convergence are studied in detail. Advantages and drawbacks of the representations as well as properties of both kinds of convergence are discussed. Numerical approximation algorithms related to piecewise power-law representations are described in Appendix.
