Patent ID: 9390065
Filing Date: 2016-07-12
CPC Classification: G06F,G06K

Claim Text:
1. An estimating computer system for iteratively estimating an unknown parameter of a model or state of a system comprising: an input module that has a configuration that receives numerical data about the system; a noise module that has a configuration that generates random, chaotic, or other type of numerical perturbations of the received numerical data or that generates pseudo-random noise; an estimation module that has a configuration that iteratively estimates the unknown parameter of the model or state of the system based on the received numerical data and that uses the numerical perturbations or the pseudo-random noise and the input numerical data during at least one of the iterative estimates of the unknown parameter; and a signaling module that has a configuration that signals when successive parameter estimates or information derived from successive parameter estimates differ by less than a predetermined signaling threshold or when the number of estimation iterations reaches a predetermined number, wherein: the estimation module has a configuration that estimates the unknown parameter of the model or state of the system using maximum likelihood, expectation-maximization, minorization-maximization, or another statistical optimization or sub-optimization method, the noise module has a configuration that generates random, chaotic, or other type of numerical perturbations of the input numerical data that fully or partially satisfy a noisy expectation maximization (NEM) condition; and the estimation module has a configuration that estimates the unknown parameter of the model or state of the system by adding, multiplying, or otherwise combining the received numerical data with the numerical perturbations; wherein the parameter estimates are used in one of nonlinear signal processing, statistical signal processing, pattern recognition and noise enhanced clustering.