Autoreclosure in extra high voltage lines using Taguchi’s method and optimized neural networks

Zahlay, F. Desta; Rao, K. Srinivasa; Baloch, T.M.

doi:10.1109/epc.2008.4763325

Cited by 2 publications

(3 citation statements)

References 17 publications

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“…The LM algorithm searches for a multivariate local minimum. 22 The algorithm modifies network weights to reduce the objective function. 22 Regarding speed and accuracy, the LM algorithm outperforms other approaches in practice.…”

Section: Methodsmentioning

confidence: 99%

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A study on deep drawn cups and the selection of optimal settings deploying ANN training and architectural parameters using the Taguchi ARAS approach

Sivam

Rajendran

2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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In recent years, the use of artificial neural networks (ANN) for modeling and optimizing metal forming processes has gained popularity. Numerous benefits are provided by ANNs, which can only be attained by constructing a high-performance ANN model. However, selecting an ANN’s appropriate training and architectural parameters remains challenging. Typically, these parameters are chosen using a trial-and-error approach in which many ANN models are produced and evaluated. This article describes the use of the Taguchi method for optimizing an ANN model trained on architectural parameters by the Levenberg–Marquardt algorithm and the additive ratio assessment method for selecting the optimized parameter. The method’s operation is demonstrated using the Altair Inspire form, the Forming Limit Diagram, and the Maximum Thinning Rate. The ANN model was trained using the orthogonal array, and its prediction performance was compared to the experimental validation. Analysis of variance assisted in determining whether the model was significant. The analysis of means helped us in deciding the appropriate ANN level for each parameter. Taguchi orthogonal array, ANN optimized for additive ratio assessment method. This model was developed and was found to be the most accurate. Through learning and testing, it was demonstrated that a systematic method for selecting the optimal ANN training and architecture parameters saves time and money by avoiding trial and error. The Taguchi-based additive ratio assessment method algorithm can then be used to determine the ideal processing parameters.

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Section: Methodsmentioning

confidence: 99%

“…22 The algorithm modifies network weights to reduce the objective function. 22 Regarding speed and accuracy, the LM algorithm outperforms other approaches in practice. This high-performance approach can converge in seconds faster than traditional methods.…”

Section: Methodsmentioning

confidence: 99%

A study on deep drawn cups and the selection of optimal settings deploying ANN training and architectural parameters using the Taguchi ARAS approach

Sivam

Rajendran

2022

Proceedings of the Institution of Mechanical Engineers, Part C:

View full text Add to dashboard Cite

show abstract

“…The Levenberg-Marquardt (LM) algorithm is an iterative method for finding the local minimum of a multivariate function defined as the sum of squares of many non-linear, real-valued functions (15). In practice, the LM algorithm is quicker and produces better solutions to a wide range of issues than other common approaches.…”

Section: Ann Training Parametersmentioning

confidence: 99%

Optimization of ANN Architecture and Training Parameters Using Taguchi Method

Vora¹

2022

ECS Trans.

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Artificial neural networks (ANNs) have gained significant popularity for modeling and optimization. Choosing appropriate training and design parameters for an ANN, on the other hand, is still a difficult task. These parameters are typically chosen by a trial-and-error strategy in which a large number of ANN models are developed and compared. This evidence stated how the Taguchi approach can be used to enhance an ANN model. To show the technique, a case study of a minimization of Green Sand-casting defects in a green sand-casting process is provided. Total 175 samples were taken for the study. L18 orthogonal array was used to construct the ANN training and architectural parameters. A Taguchi-optimized ANN model was created and showed excellent prediction accuracy. Analyses and experiments have shown that the best ANN training and architectural parameters may be found in a systematic manner, minimizing the time-consuming trial-and-error method.

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Autoreclosure in extra high voltage lines using Taguchi’s method and optimized neural networks

Cited by 2 publications

References 17 publications

A study on deep drawn cups and the selection of optimal settings deploying ANN training and architectural parameters using the Taguchi ARAS approach

A study on deep drawn cups and the selection of optimal settings deploying ANN training and architectural parameters using the Taguchi ARAS approach

Optimization of ANN Architecture and Training Parameters Using Taguchi Method

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