Mahalanobis-Taguchi system: state of the art

Mota-Gutiérrez, Cecilia G.; Reséndiz‐Flores, Edgar O.; Reyes-Carlos, Yadira Iracema

doi:10.1108/ijqrm-10-2016-0174

Cited by 19 publications

(17 citation statements)

References 52 publications

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“…The Taguchi method is a well-celebrated technique that provides a structured and efficient methodology to streamline a process [25]. Significance of the application of Taguchi methods and its variants in different industrial applications is evident from the works of Mota-Gutiérrez et al [26], Jeyapaul et al [27], Beyer and Sendhoff [28], and Bendell et al [29]. Among others, Taguchi's approach was considered by the researchers to address process improvement in food [30][31][32], healthcare [33], electronics [34], and manufacturing [35,36] industries.…”

Section: Methodsmentioning

confidence: 99%

Optimising Parameters for Expanded Polystyrene Based Pod Production Using Taguchi Method

et al. 2019

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Expanded polystyrene (EPS) is used in the building and construction industry for insulation and under flooring purposes. The objective of the study is to investigate the impact of the application of the total quality management (TQM) technique on the significant parameters of the pod production process in a New Zealand based EPS manufacturing facility. In this work, Taguchi’s L27 orthogonal array (OA) is considered for conducting experiments through three input parameters i.e., weight of untreated beads, batch duration, and temperature is investigated. Based on the results, the analyses are carried out while using statistical approaches, such as analysis of the means (ANOM) and analysis of variance (ANOVA). The results from confirmatory experiment indicate that, at optimal parameters setting (17 kg of untreated bead, 130 s of batch duration and 155 °F of temperature), a reasonably streamlined pod manufacturing process can be achieved for sustainable operations.

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Section: Methodsmentioning

confidence: 99%

Optimising Parameters for Expanded Polystyrene Based Pod Production Using Taguchi Method

et al. 2019

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“…Due to minimal resources within the T-Method itself, the authors are relying on the progress of OA enhancement within the MT-Method for practical purposes. The most recent study conducted by Mota-Gutiérrez et al [16] which summarised the 18 year progress of the MTmethod in various industrial practices was found to be very helpful for this study since the area of dimensional reduction or variables optimization by several researchers are still being progressively enhanced till recently [7], [17][18]. This study provides a conceptual idea in applying the same concept to T-Method variable screening or optimization since very few studies have been conducted within that area.…”

Section: Related Studiesmentioning

confidence: 96%

Binary Particle Swarm Optimization for Variables Selection Optimization in Taguchi’s T-Method

et al. 2020

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Prediction analysis has drawn significant interest in numerous field. Taguchi’s T-Method is a prediction tool that developed practically but not limited to small sample analysis. It was developed explicitly for multidimensional system prediction by relying on historical data as the baseline model and adapting the signal to noise ratio (SNR) as well as zero proportional concepts in strengthening its robustness. Orthogonal array (OA) in T-Method is a variable selection optimization technique in improving the prediction accuracy as well as help in eliminating variables that may deteriorate the overall performance. However, the limitation of OA in dealing with higher multidimensionality restraint the optimization accuracy. Binary particle swarm optimization used in this study helps to cater to the limitation of OA as well as optimizing the variable selection process to better prediction accuracy. The results show that if the historical data consist of samples with higher correlation of determination (R2) value for the model creation, the optimization process in reducing the number of variables would be much reliable and accurate. Comparing between T-Method+OA and T-Method+BPSO in four different case study, it shows that T-Method+BPSO performing better with greater R2 and means relative error (MRE) value compared to T-Method+OA.

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“…The MD can be easily defined using the covariance matrix of learning samples and can be calculated faster and more easily than neural networks (NNs) and support vector machines (SVMs). The MD is still widely used for industrial and various other purposes (see a recent example [3]), and there have been cases in which the MD was said to have better recognition performance than NNs and SVMs if there are few learning samples [4][5][6][7]. Usually, the population covariance matrix Σ of the learning samples is unknown in advance.…”

Section: Introductionmentioning

confidence: 99%

Improved method for correcting sample Mahalanobis distance without estimating population eigenvalues or eigenvectors of covariance matrix

Kobayashi

2019

Int J Data Sci Anal

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The recognition performance of the sample Mahalanobis distance (SMD) deteriorates as the number of learning samples decreases. Therefore, it is important to correct the SMD for a population Mahalanobis distance (PMD) such that it becomes equivalent to the case of infinite learning samples. In order to reduce the computation time and cost for this main purpose, this paper presents a correction method that does not require the estimation of the population eigenvalues or eigenvectors of the covariance matrix. In short, this method only requires the sample eigenvalues of the covariance matrix, number of learning samples, and dimensionality to correct the SMD for the PMD. This method involves the summation of the SMD's principal components (each of which is divided by its expectation obtained using the delta method), Lawley's bias estimation, and the variances of the sample eigenvectors. A numerical experiment demonstrates that this method works well for various cases of learning sample number, dimensionality, population eigenvalues sequence, and non-centrality. The application of this method also shows improved performance of estimating a Gaussian mixture model using the expectation-maximization algorithm.

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Mahalanobis-Taguchi system: state of the art

Cited by 19 publications

References 52 publications

Optimising Parameters for Expanded Polystyrene Based Pod Production Using Taguchi Method

Optimising Parameters for Expanded Polystyrene Based Pod Production Using Taguchi Method

Binary Particle Swarm Optimization for Variables Selection Optimization in Taguchi’s T-Method

Improved method for correcting sample Mahalanobis distance without estimating population eigenvalues or eigenvectors of covariance matrix

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