Artificial Neural Network Prosperities in Textile Applications

Tehran, Mohammad Amani; Maleki, Mahboubeh

doi:10.5772/16095

Cited by 4 publications

(1 citation statement)

References 59 publications

(108 reference statements)

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“…5. Ui fault tolerance criterion helps us get extra information about ANN features and it can be recommended for application both in theoretical research and engineering practice including information processing in industrial controlling and measuring systems [5] of any facilities.…”

Section: Discussionmentioning

confidence: 99%

Numerical Simulation of Neural Network Components of Controlling and Measuring Systems

Danilin

Makarov

Shchanikov

2015

AMM

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The article deals with the problem of calculating the fault tolerance of neural network components of industrial controlling and measuring systems used in mechanical engineering. We have formulated a general approach to developing methods for quantitative determination of the level of the fault tolerance of artificial neural networks with any structure and function. We have studied the fault tolerance of four artificial feedforward neural networks as well as the correlation between the result of determining the fault tolerance level and a selected performance parameter of artificial neural networks.

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Section: Discussionmentioning

confidence: 99%

Numerical Simulation of Neural Network Components of Controlling and Measuring Systems

Danilin

Makarov

Shchanikov

2015

AMM

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Drug release profile in core–shell nanofibrous structures: A study on Peppas equation and artificial neural network modeling

Maleki

Tehran

Latifi

et al. 2014

Computer Methods and Programs in Biomedicine

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Neural network model applied to electromagnetic shielding effectiveness of ultra-light Ni/Cu coated polyester fibrous materials

Periyasamy

Muthusamy

Militký

2022

Sci Rep

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The purpose of effective electromagnetic interference (EMI) shielding is to prevent EMI from smartphone, wireless, and utilization of other electronic devices. The electrical conductivity of materials strongly influences on the EMI shielding properties. In this work, mainly focus to predict the EMI shielding effectiveness on the ultralight weight fibrous materials by artificial neural network (ANN). Prior to the ANN modelling, the ultra-lightweight fibrous materials were electroplated with different concentration of Ni/Cu and then coated with different silanes. This work utilizes the algorithm to provide accurate quantitative values of EMI shielding effectiveness (EM SE). To compare its performance, the experimental and the predicted EM SE values were validated by root-mean-square error (RMSE), mean absolute percentage error (MAPE) values and correlation coefficient ‘r’. The proposed ANN results accurately predict the experimental data with correlation coefficients of 0.991 and 0.997. Further due to its simplicity, reliability as well as its efficient computational capability the proposed ANN model permits relatively fast, cost effective and objective estimates to be made of serving in this industry.

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Artificial Neural Network Prosperities in Textile Applications

Cited by 4 publications

References 59 publications

Numerical Simulation of Neural Network Components of Controlling and Measuring Systems

Numerical Simulation of Neural Network Components of Controlling and Measuring Systems

Drug release profile in core–shell nanofibrous structures: A study on Peppas equation and artificial neural network modeling

Neural network model applied to electromagnetic shielding effectiveness of ultra-light Ni/Cu coated polyester fibrous materials

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