Predictive models of tensile strength and disintegration time for simulated Chinese herbal medicine extracts compound tablets based on artificial neural networks

In the present work, artificial neural networks (ANN) have been used to model the complex relationship between input-output parameters of metal inert gas (MIG) welding processes. Four ANN training algorithms such as back propagation neural network (BPNN) with gradient descent momentum (GDM), BPNN with Levenberg Marquardt (LM) algorithm, BPNN with Bayesian regularization (BR), and radial basis function networks (RBFN) method have been used for prediction modelling. An experimentation based on full factorial experimental design has been conducted on MIG welding of austenitic stainless steel of grade-304 where welding current, welding speed, and voltage have been considered as input parameters, and tensile strength has been considered as measurable output parameter. The dataset so constituted is used for ANN modelling. Altogether, 40 different ANN architectures have been trained and tested using the above-mentioned algorithms, and 3-11-1 ANN architecture trained using BPNN with BR has been considered to show best prediction capability with mean % absolute error of 0.354%.

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Künstliche neuronale Netze für das Feinschneiden*/Artificial neural networks for fine blanking - A methodology for the development of predictive models for fine blanking

wt Werkstattstechnik online ◽

10.37544/1436-4980-2017-10-41 ◽

2017 ◽

Vol 107 (10) ◽

pp. 719-724

Author(s):

F. Prof. Klocke ◽

J. Stanke ◽

D. Trauth ◽

P. Mattfeld ◽

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Predictive Models ◽

Neuronale Netze ◽

Fine Blanking ◽

Current Trends ◽

New Challenges ◽

Artificial Neural ◽

Künstliche Neuronale Netze ◽

Blanking Process

Höhere Anforderungen an die Produktivität und aktuelle Trends stellen den Feinschneidprozess vor neue Herausforderungen. Infolgedessen ist eine optimierte Prozessführung notwendig. Ein Modell, welches großes Potenzial für eine Optimierung der Prozessführung bietet, sind künstliche neuronale Netze. Dieser Fachbeitrag stellt eine Methodik zur Nutzung von künstlichen neuronalen Netzen für die Optimierung des Feinschneidprozesses vor.   Higher demands on productivity and current trends pose new challenges to the fine blanking process. They require an optimization of the process realization. One model that offers great potential for the optimization of the process realization are artificial neural networks. This article provides a methodology for the use of artificial neural networks for the optimization of the fine blanking process.

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Prediction of short and medium term PM10 concentration using artificial neural networks

Management of Environmental Quality An International Journal ◽

10.1108/meq-03-2018-0055 ◽

2019 ◽

Vol 30 (2) ◽

pp. 414-436 ◽

Cited By ~ 5

Author(s):

Elaine Schornobay-Lui ◽

Eduardo Carlos Alexandrina ◽

Mônica Lopes Aguiar ◽

Werner Siegfried Hanisch ◽

Edinalda Moreira Corrêa ◽

...

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Predictive Models ◽

Urban Areas ◽

Meteorological Data ◽

High Volume ◽

Data Series ◽

Content Type ◽

Artificial Neural ◽

Narx Model

Purpose There has been a growing concern about air quality because in recent years, industrial and vehicle emissions have resulted in unsatisfactory human health conditions. There is an urgent need for the measurements and estimations of particulate pollutants levels, especially in urban areas. As a contribution to this issue, the purpose of this paper is to use data from measured concentrations of particulate matter and meteorological conditions for the predictions of PM10. Design/methodology/approach The procedure included daily data collection of current PM10 concentrations for the city of São Carlos-SP, Brazil. These data series enabled to use an estimator based on artificial neural networks. Data sets were collected using the high-volume sampler equipment (VFA-MP10) in the period ranging from 1997 to 2006 and from 2014 to 2015. The predictive models were created using statistics from meteorological data. The models were developed using two neural network architectures, namely, perceptron multilayer (MLP) and non-linear autoregressive exogenous (NARX) inputs network. Findings It was observed that, over time, there was a decrease in the PM10 concentration rates. This is due to the implementation of more strict environmental laws and the development of less polluting technologies. The model NARX that used as input layer the climatic variables and the PM10 of the previous day presented the highest average absolute error. However, the NARX model presented the fastest convergence compared with the MLP network. Originality/value The presentation of a given PM10 concentration of the previous day improved the performance of the predictive models. This paper brings contributions with the NARX model applications.

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