Identification of microbiota dynamics using robust parameter estimation methods

Robust Identification Using Semiquantitative Methods

While model-based process monitoring and system identiication share the common goal of describing the behavior of a physical system based on a mathematical model, the assumptions made by system identiication are typically too strong to apply it directly to monitoring problems. This article describes SQUID, a new system identiication method that uses refutation rather than search to identify a m...

Robust Parameter Estimation in ComputerVisionCharles

Bayesian Methods of Parameter Estimation

The frequentest approach is the classical approach to parameter estimation. It assumes that there is an unknown but objectively fixed parameter θ [3]. It chooses the value of θ which maximizes the likelihood of observed data [4], in other words, making the available data as likely as possible. A common example is the maximum likelihood estimator (MLE). The frequentest approach is statistically ...

Parameter Estimation of Loranz Chaotic Dynamic System Using Bees Algorithm

An important problem in nonlinear science is the unknown parameters estimation in Loranz chaotic system. Clearly, the parameter estimation for chaotic systems is a multidimensional continuous optimization problem, where the optimization goal is to minimize mean squared errors (MSEs) between real and estimated responses for a number of given samples. The Bees algorithm (BA) is a new member of me...

Using Hierarchical Probability Models to Evaluate Robust Parameter Design Methods

A method is proposed for evaluating the effectiveness of robust parameter design methods. A hierarchical probability model is presented that enables an investigator to represent assumptions about regularities in system responses such as effect sparsity, hierarchy, and inheritance. The hierarchical probability model is subsequently used to create a population of responses to which alternative ro...