A probabilistic artificial neural network-based procedure for variance change point estimation

Amiri, Amirhossein; Niaki, Seyed Taghi Akhavan; Moghadam, Alireza Taheri

doi:10.1007/s00500-014-1293-x

Cited by 13 publications

(3 citation statements)

References 21 publications

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“…It is recommended to calculate the average and the standard deviation of the performance metrics in all repetitions. Hypothesis tests should be performed to check the significance of differences between ANN configurations (Amiri et al 2014;Singh et al 2015;Anwar et al 2016). Statistically significant differences were not observed for architectures with hidden layers of between 25 and 50 neurons.…”

Section: Second Step: Selection Of the Best Ann Architecturementioning

confidence: 99%

Automated early detection of drops in commercial egg production using neural networks

Ramírez-Morales

Fernández-Blanco

Rivero

2017

British Poultry Science

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ABSTRACT1. The purpose of this work was to support decision-making in poultry farms by performing automatic early detection of anomalies in egg production. 2. Unprocessed data were collected from a commercial egg farm on a daily basis over 7 years. Records from a total of 24 flocks, each with approximately 20 000 laying hens, were studied. 3. Other similar works have required a prior feature extraction by a poultry expert, and this method is dependent on time and expert knowledge. 4. The present approach reduces the dependency on time and expert knowledge because of the automatic selection of relevant features and the use of artificial neural networks capable of costsensitive learning. 5. The optimum configuration of features and parameters in the proposed model was evaluated on unseen test data obtained by a repeated cross-validation technique. 6. The accuracy, sensitivity, specificity and positive predictive value are presented and discussed at 5 forecasting intervals. The accuracy of the proposed model was 0.9896 for the day before a problem occurs.

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Section: Second Step: Selection Of the Best Ann Architecturementioning

confidence: 99%

Automated early detection of drops in commercial egg production using neural networks

Ramírez-Morales

Fernández-Blanco

Rivero

2017

British Poultry Science

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“…The problem of detecting changes in time series has received a great deal of attention in recent years, with applications in finance (Schröder and Fryzlewicz, 2013), quality control (Amiri et al, 2015), climate modelling (Reeves et al, 2007;, genome sequencing (Muggeo and Adelfio, 2011;Caron et al, 2012), neuroscience (Anastasiou et al, 2022), and epidemic modelling (Jiang et al, 2023), amongst many others. Various methods have been developed for detecting different types of change, for example changes in mean, variance, and slope.…”

Section: Introductionmentioning

confidence: 99%

Post-selection inference for quantifying uncertainty in changes in variance

Carrington,

Fearnhead

2024

Preprint

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Quantifying uncertainty in detected changepoints is an important problem. However it is challenging as the naive approach would use the data twice, first to detect the changes, and then to test them. This will bias the test, and can lead to anti-conservative p-values. One approach to avoid this is to use ideas from post-selection inference, which conditions on the information in the data used to choose which changes to test. As a result this produces valid p-values; that is, p-values that have a uniform distribution if there is no change. Currently such methods have been developed for detecting changes in mean only. This paper presents two approaches for constructing post-selection p-values for detecting changes in variance. These vary depending on the method use to detect the changes, but are general in terms of being applicable for a range of change-detection methods and a range of hypotheses that we may wish to test.

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“…Assareh et al (2013) applied Bayesian hierarchical models to estimate change point where there exists a step change, a linear trend and a known multiple number of changes in the Poisson quality characteristic. Amiri et al (2014) developed a probabilistic neural network (PNN)-based procedure to estimate the variance change point in a univariate process with normal quality characteristic. For more information, refer to the review paper provided by Amiri and Allahyari (2012).…”

Section: Introductionmentioning

confidence: 99%

Step change point estimation in the multivariate-attribute process variability using artificial neural networks and maximum likelihood estimation

2015

Self Cite

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In some statistical process control applications, the combination of both variable and attribute quality characteristics which are correlated represents the quality of the product or the process. In such processes, identification the time of manifesting the out-of-control states can help the quality engineers to eliminate the assignable causes through proper corrective actions. In this paper, first we use an artificial neural network (ANN)-based method in the literature for detecting the variance shifts as well as diagnosing the sources of variation in the multivariate-attribute processes. Then, based on the quality characteristics responsible for the out-of-control state, we propose a modular model based on the ANN for estimating the time of step change in the multivariate-attribute process variability. We also compare the performance of the ANNbased estimator with the estimator based on maximum likelihood method (MLE). A numerical example based on simulation study is used to evaluate the performance of the estimators in terms of the accuracy and precision criteria. The results of the simulation study show that the proposed ANN-based estimator outperforms the MLE estimator under different out-of-control scenarios where different shift magnitudes in the covariance matrix of multivariateattribute quality characteristics are manifested.

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A probabilistic artificial neural network-based procedure for variance change point estimation

Cited by 13 publications

References 21 publications

Automated early detection of drops in commercial egg production using neural networks

Automated early detection of drops in commercial egg production using neural networks

Post-selection inference for quantifying uncertainty in changes in variance

Step change point estimation in the multivariate-attribute process variability using artificial neural networks and maximum likelihood estimation

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