Parameter Estimation Strategies in Thermodynamics

Linear Parameter Estimation : Asymptotically Efficient Adaptive Strategies

This paper considers the problem of distributed adaptive linear parameter estimation in multiagent inference networks. Local sensing model information is only partially available at the agents, and interagent communication is assumed to be unpredictable. The paper develops a generic mixed time-scale stochastic procedure consisting of simultaneous distributed learning and estimation, in which th...

Local versus global strategies in multi-parameter estimation

P. A. Knott, ∗ T. J. Proctor, 3, ∗ A. J. Hayes, J. F. Ralph, P. Kok, and J. A. Dunningham Department of Physics and Astronomy, University of Sussex, Brighton BN1 9QH, UK School of Physics and Astronomy, University of Leeds, Leeds, LS2 9JT, UK Berkeley Quantum Information and Computation Center, Department of Chemistry, University of California, Berkeley, CA 94720, USA Department of Electrical E...

One-parameter Darboux transformations in Thermodynamics

The quantum oscillator thermodynamic actions are the conjugate intensive parameters for the frequency in any frequency changing process. These oscillator actions fulfill simple Riccati equations. Interesting Darboux transformations of the fundamental Planck and pure vacuum actions are discussed here in some detail. It is shown that the one-parameter “Darboux-Transformed-Thermodynamics” refers t...

Distributed conjugate gradient strategies for parameter estimation over sensor networks

This paper presents distributed adaptive algorithms based on the conjugate gradient (CG) method for distributed networks. Both incre-mental and diffusion adaptive solutions are all considered. The distributed conventional (CG) and modified CG (MCG) algorithms have an improved performance in terms of mean square error as compared with least-mean square (LMS)-based algorithms and a performance th...

Global Nonlinear Parameter Estimation Using Interval Analysis: Parallel Computing Strategies