Comparison between two types of Artificial Neural Networks used for validation of pharmaceutical processes

Behzadi, Sharareh Salar; Prakasvudhisarn, Chakguy; Klocker, Johanna; Wolschann, Peter; Viernstein, Helmut

doi:10.1016/j.powtec.2009.05.025

Cited by 44 publications

(20 citation statements)

References 37 publications

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“…As the network architecture of the GR is determined by the size of the dataset and the number of causal factors, it is precise and robust. A previous study also found that prediction accuracy was greater with GR than with MLP 18. The LIN model only had high predictive ability for TS B .…”

mentioning

confidence: 74%

Creation of a Tablet Database Containing Several Active Ingredients and Prediction of Their Pharmaceutical Characteristics Based on Ensemble Artificial Neural Networks

Takagaki

Arai

Takayama

2010

Journal of Pharmaceutical Sciences

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mentioning

confidence: 74%

Creation of a Tablet Database Containing Several Active Ingredients and Prediction of Their Pharmaceutical Characteristics Based on Ensemble Artificial Neural Networks

Takagaki

Arai

Takayama

2010

Journal of Pharmaceutical Sciences

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“…ANN can process a large amount of data simultaneously, establishing the relationship between the input and output of the system through learning. This systematic model has been widely employed in various areas for calculations and predictions [19]. ANN is supervised learning network featuring high accuracy, high learning speed, and capacities to use continuous values as input, handle complex sample identification, and process nonlinear function compositions.…”

Section: Theorymentioning

confidence: 99%

Regional Forecasting of Wind Speeds during Typhoon Landfall in Taiwan: A Case Study of Westward-Moving Typhoons

et al. 2018

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Abstract:Taiwan is located on a route where typhoons often strike. Each year, the strong winds accompanying typhoons are a substantial threat and cause significant damage. However, because the terrains of high mountains in Taiwan vary greatly, when a typhoon passes the Central Mountain Range (CMR), the wind speed of typhoons becomes difficult to predict. This research had two primary objectives: (1) to develop data-driven techniques and a powerful artificial neural network (ANN) model to predict the highly complex nonlinear wind systems in western Taiwan; and, (2) to investigate the accuracy of wind speed predictions at various locations and for various durations in western Taiwan when the track of westward typhoons is affected by the complex geographical shelters and disturbances of the CMR. This study developed a typhoon wind speed prediction model that evaluated various typhoon tracks (covering Type 2, Type 3, and Type 4 tracks, as defined by the Central Weather Bureau), and evaluated the prediction accuracy at Hsinchu, Wuqi, and Kaohsiung Stations in western Taiwan. Back propagation neural networks (BPNNs) were employed to establish wind speed prediction models, and a linear regression model was adopted as the benchmark to evaluate the strengths and weaknesses of the BPNNs. The results were as follows: (1) The BPNNs generally had favorable performance in predicting wind speeds and their performances were superior to linear regressions; (2) when absolute errors were adopted to evaluate the prediction performances, the predictions at Hsinchu Station were the most accurate, whereas those at Wuqi Station were the least accurate; however, when relative errors were adopted, the predictions at Hsinchu Station were again the most accurate, whereas those at Kaohsiung were the least accurate; and, (3) regarding the relative error rates for the maximum wind speed of Types 2, 3, and 4 typhoons, Wuqi, Kaohsiung, and Wuqi had the most accurate performance, respectively; as for maximum wind time error (ET M ) for Types 2, 3, and 4 typhoons, Kaohsiung, Wuqi, and Wuqi correspondingly performed the most favorably.

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“…Learning of the considered Multilayer Perceptron, is done using the backpropagation algorithm, the modification of network weights is done by the Levenberg-Marquardt method, it is designated in MATLAB ® by TRAINLM [20,21].…”

Section: Fig 5 Building the Neural Networkmentioning

confidence: 99%

Coupling the image analysis and the artificial neural networks to predict a mixing time of a pharmaceutical powder

Mahdi¹,

Mouhi²,

N³

et al. 2018

J. Fundam and Appl Sci.

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In recent years, different laboratories were interested in predicting the mixing time of a pharmaceutical powder. In fact, a nonhomogeneous mixture may lead to under dose and/or overdose of the active ingredient in the drug product. Our study is aimed toward using a new and revolutionary approach in the field of the processes "The Artificial Neural Networks" (ANN) by using the Neural Networks Toolbox TM derived from Matlab ® software. The validation of the neural network was assumed by studying others mixing p o w d e r s and then w e compared the experimental results to the data obtained by the neural network calculations. Experimental results were obtained from a non-destructive method (Image Analysis) which was used in order to characterize the homogeneity of powder mixture in a V-Blender as well as a Cubic Blender which are most used in the pharmaceutical industry. Keywords

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Comparison between two types of Artificial Neural Networks used for validation of pharmaceutical processes

Cited by 44 publications

References 37 publications

Creation of a Tablet Database Containing Several Active Ingredients and Prediction of Their Pharmaceutical Characteristics Based on Ensemble Artificial Neural Networks

Creation of a Tablet Database Containing Several Active Ingredients and Prediction of Their Pharmaceutical Characteristics Based on Ensemble Artificial Neural Networks

Regional Forecasting of Wind Speeds during Typhoon Landfall in Taiwan: A Case Study of Westward-Moving Typhoons

Coupling the image analysis and the artificial neural networks to predict a mixing time of a pharmaceutical powder

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