Design of an adaptive maximum likelihood estimator for key parameters in macroscopic traffic flow model based on expectation maximum algorithm

Ramezani, Amin; Moshiri, Behzad; Khan, Aysha; Abdulhai, Baher

doi:10.1049/iet-smt.2010.0122

Cited by 4 publications

(1 citation statement)

References 17 publications

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“…Chen et al [30] introduced particle calculation into PSO to optimize the nonlinear model composed of multiple regression models. Sheng et al [31] adopted expectation maximization (EM) [32], a common approach to estimate of the optimal super parameters, to optimize the Gaussian mixture regression for estimating the charge of electric vehicles. Such studies at present mainly focus on optimizing parameters in the model, but research for a mixture of the better model structure and the related parameters has not been reported.…”

Section: Optimization Machanism For Model Parametersmentioning

confidence: 99%

A Genetic Programming-Driven Data Fitting Method

et al. 2020

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Data fitting is the process of constructing a curve, or a set of mathematical functions, that has the best fit to a series of data points. Different with constructing a fitting model from same type of function, such as the polynomial model, we notice that a hybrid fitting model with multiple types of function may have a better fitting result. Moreover, this also shows better interpretability. However, a perfect smooth hybrid fitting model depends on a reasonable combination of multiple functions and a set of effective parameters. That is a high-dimensional multi-objective optimization problem. This paper proposes a novel data fitting model construction approach. In this approach, the model is expressed by an improved tree coding expression and constructed through an evolution search process driven by the genetic programming. In order to verify the validity of generated hybrid fitting model, 6 prediction problems are chosen for experiment studies. The experimental results show that the proposed method is superior to 7 typical methods in terms of the prediction accuracy and interpretability. INDEX TERMS Data fitting, hybrid model, genetic programming, tree coding, interpretability.

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Section: Optimization Machanism For Model Parametersmentioning

confidence: 99%