Health Assessment of Automotive Batteries Through Computational Intelligence-Based Soft Sensors: An Empirical Study

2017 ◽  
pp. 47-56 ◽  
Author(s):  
Eva Almansa ◽  
David Anseán ◽  
Inés Couso ◽  
Luciano Sánchez
Keyword(s):  
Empirical Study ◽  
Computational Intelligence ◽  
Health Assessment ◽  
Soft Sensors ◽  
Automotive Batteries

Health assessment of LFP automotive batteries using a fractional-order neural network

2020 ◽  
Vol 391 ◽  
pp. 345-354
Author(s):  
Luciano Sánchez ◽  
José Otero ◽  
David Anseán ◽  
Inés Couso
Keyword(s):  
Neural Network ◽  
Fractional Order ◽  
Health Assessment ◽  
Automotive Batteries

scholarly journals Empirical Study of Homogeneous and Heterogeneous Ensemble Models for Software Development Effort Estimation

2013 ◽  
Vol 2013 ◽  
pp. 1-21 ◽  
Author(s):  
Mahmoud O. Elish ◽  
Tarek Helmy ◽  
Muhammad Imtiaz Hussain
Keyword(s):  
Empirical Study ◽  
Software Development ◽  
Computational Intelligence ◽  
Accurate Estimation ◽  
Development Effort ◽  
Effort Estimation ◽  
Ensemble Models ◽  
Intelligence Models ◽  
Software Development Effort ◽  
Software Development Effort Estimation

Accurate estimation of software development effort is essential for effective management and control of software development projects. Many software effort estimation methods have been proposed in the literature including computational intelligence models. However, none of the existing models proved to be suitable under all circumstances; that is, their performance varies from one dataset to another. The goal of an ensemble model is to manage each of its individual models’ strengths and weaknesses automatically, leading to the best possible decision being taken overall. In this paper, we have developed different homogeneous and heterogeneous ensembles of optimized hybrid computational intelligence models for software development effort estimation. Different linear and nonlinear combiners have been used to combine the base hybrid learners. We have conducted an empirical study to evaluate and compare the performance of these ensembles using five popular datasets. The results confirm that individual models are not reliable as their performance is inconsistent and unstable across different datasets. Although none of the ensemble models was consistently the best, many of them were frequently among the best models for each dataset. The homogeneous ensemble of support vector regression (SVR), with the nonlinear combiner adaptive neurofuzzy inference systems-subtractive clustering (ANFIS-SC), was the best model when considering the average rank of each model across the five datasets.

Individuals without jobs: An empirical study of job-seeking behavior and reemployment.

1996 ◽  
Vol 81 (1) ◽  
pp. 76-87 ◽  
Author(s):  
Connie R. Wanberg ◽  
John D. Watt ◽  
Deborah J. Rumsey
Keyword(s):  
Empirical Study ◽  
Job Seeking ◽  
Seeking Behavior

Psychology: An empirical study of behavior.

1933 ◽  
Author(s):  
Frederick H. Lund
Keyword(s):  
Empirical Study
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