Model selection through a statistical analysis of the global minimum of a weighted nonlinear least squares cost function

Pintelon, R.; Schoukens, Johan; Vandersteen, Gerd

doi:10.1109/78.558486

Cited by 18 publications

(2 citation statements)

References 18 publications

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“…For instance, weights have been considered in the cost functions (Shadmand et al, 2019) for attaining multiple control objectives for power electronic interfaces. Co-variance weighted nonlinear least-square cost functions are presented in Pintelon et al (1997) for model identification applications. Time-delay estimation over highly oscillatory objective functions, with applications in sonar, as observed in Wu and Li (1998), has been attained for Cramer-Rao bounds [see also Toh and Eng (2008) for weighted least-square learning].…”

Section: Problem Formulationmentioning

confidence: 99%

Optimality Condition and Distributed Optimization for Economic Dispatch Using a Novel Weighted Incremental Cost Consensus Approach

et al. 2022

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This research report investigates a novel optimization approach for the economic dispatch problem (EDP) based on the weighted sum of generators’ costs under supply-demand balance. Unlike conventional approaches, we present a distributed optimization approach that ensures optimality using weighted incremental cost (IC) consensus and sign-consensus error convergence. We can apply the optimization of a weighted sum of generators’ costs to address several constraints, such as capacity and environmental constraints, in addition to the supply-demand balance. The proposed distributed weighted incremental cost consensus approach has been applied to the IEEE-30 bus and IEEE-118 bus systems over a communication topology. The results indicate the efficacy of weights to address generation constraints and the convergence of weighted ICs under supply-demand balance.

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Section: Problem Formulationmentioning

confidence: 99%

Optimality Condition and Distributed Optimization for Economic Dispatch Using a Novel Weighted Incremental Cost Consensus Approach

et al. 2022

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“…The inadequate choice of an initial function may result in models that fail to accurately capture the intrinsic dynamics of the data. This limitation becomes particularly apparent in intricate biological processes like rumen fermentation, where these methods may not offer reliable insights or predictions [17,18].…”

Section: Introductionmentioning

confidence: 99%

Development of an Alternative In Vitro Rumen Fermentation Prediction Model

Wang,

Zhou,

Jiang

et al. 2024

Animals

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The aim of this study is to identify an alternative approach for simulating the in vitro fermentation and quantifying the production of rumen methane and rumen acetic acid during the rumen fermentation process with different total mixed rations. In this experiment, dietary nutrient compositions (neutral detergent fiber (NDF), acid detergent fiber (ADF), crude protein (CP), and dry matter (DM)) were selected as input parameters to establish three prediction models for rumen fermentation parameters (methane and acetic acid): an artificial neural network model, a genetic algorithm-bp model, and a support vector machine model. The research findings show that the three models had similar simulation results that aligned with the measured data trends (R2 ≥ 0.83). Additionally, the root mean square errors (RMSEs) were ≤1.85 mL/g in the rumen methane model and ≤2.248 mmol/L in the rumen acetic acid model. Finally, this study also demonstrates the models’ capacity for generalization through an independent verification experiment, as they effectively predicted outcomes even when significant trial factors were manipulated. These results suggest that machine learning-based in vitro rumen models can serve as a valuable tool for quantifying rumen fermentation parameters, guiding the optimization of dietary structures for dairy cows, rapidly screening methane-reducing feed options, and enhancing feeding efficiency.

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Determining groundwater‐surface water exchange from temperature‐time series: Combining a local polynomial method with a maximum likelihood estimator

Vandersteen

Schneidewind

Anibas

et al. 2015

Water Resources Research

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The use of temperature-time series measured in streambed sediments as input to coupled water flow and heat transport models has become standard when quantifying vertical groundwater-surface water exchange fluxes. We develop a novel methodology, called LPML, to estimate the parameters for 1-D water flow and heat transport by combining a local polynomial (LP) signal processing technique with a maximum likelihood (ML) estimator. The LP method is used to estimate the frequency response functions (FRFs) and their uncertainties between the streambed top and several locations within the streambed from measured temperature-time series data. Additionally, we obtain the analytical expression of the FRFs assuming a pure sinusoidal input. The estimated and analytical FRFs are used in an ML estimator to deduce vertical groundwater-surface water exchange flux and its uncertainty as well as information regarding model quality. The LPML method is tested and verified with the heat transport models STRIVE and VFLUX. We demonstrate that the LPML method can correctly reproduce a priori known fluxes and thermal conductivities and also show that the LPML method can estimate averaged and time-variable fluxes from periodic and nonperiodic temperature records. The LPML method allows for a fast computation of exchange fluxes as well as model and parameter uncertainties from many temperature sensors. Moreover, it can utilize a broad frequency spectrum beyond the diel signal commonly used for flux calculations.

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Model selection through a statistical analysis of the global minimum of a weighted nonlinear least squares cost function

Cited by 18 publications

References 18 publications

Optimality Condition and Distributed Optimization for Economic Dispatch Using a Novel Weighted Incremental Cost Consensus Approach

Optimality Condition and Distributed Optimization for Economic Dispatch Using a Novel Weighted Incremental Cost Consensus Approach

Development of an Alternative In Vitro Rumen Fermentation Prediction Model

Determining groundwater‐surface water exchange from temperature‐time series: Combining a local polynomial method with a maximum likelihood estimator

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