Artificial Neural Network and the Taguchi Method Application for Robust Wheatstone Bridge Design

20th Design Automation Conference: Volume 2 — Robust Design Applications; Decomposition and Design Optimization; Optimization Tools and Applications ◽

10.1115/detc1994-0110 ◽

1994 ◽

Author(s):

Jung-eui Hong ◽

Cihan H. Dagli ◽

Kenneth M. Ragsdell

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Wheatstone Bridge ◽

Parameter Design ◽

System Response ◽

Measurement Circuit ◽

Wide Range ◽

Reduce Measurement Error ◽

On Line ◽

Artificial Neural

Abstract The primary function of the Wheatstone bridge is to measure an unknown resistance. The elements of this well-known measurement circuit will take on different values depending upon the range and accuracy required for a particular application. The Taguchi approach to parameter design is used to select values for the measurement circuit elements so as to reduce measurement error. Next we introduce the use of an artificial neural network to extrapolate limited experimental results to predict system response over a wide range of applications. This approach can be employed for on-line quality control of the manufacture of such device.

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The Artificial Neural Network On-Line Monitoring Model of Boiler Efficiency

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.773.239 ◽

2013 ◽

Vol 773 ◽

pp. 239-243

Author(s):

Xin Dai ◽

Bin Yang ◽

Ya Feng Zhong ◽

Yong Hong Guo

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Monitoring Model ◽

Wide Range ◽

Precise Analysis ◽

Boiler Efficiency ◽

On Line ◽

Artificial Neural ◽

The Heat Balance ◽

Selection Of

When adjusting the borler combustion, the borler efficiency need to be constantly monitored.The traditional method of calculating boiler efficiency is complex.Based on the heat balance method,the main factors of influencing boiler efficiency was analysed deeply and the artificial neural network on-line monitoring model of boiler efficiency was established to predict boiler efficiency accurately and constantly in this paper. After precise analysis and tracking, the input variable for the artificial neural network on-line monitoring model of boiler efficiency was selected, so as to avoid larger error caused by the rough selection of input variable in the previous artificial neural network. At last,based on a 600MW boiler,the borler efficiency was predicted in this paper.we can easily know from the prediction result that the artificial neural network on-line monitoring model of boiler efficiency can predict the boiler efficiency accurately and constantly at a wide range condition.

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Predictive modelling of soils’ hydraulic conductivity using artificial neural network and multiple linear regression

SN Applied Sciences ◽

10.1007/s42452-020-03974-7 ◽

2021 ◽

Vol 3 (2) ◽

Author(s):

Charles Gbenga Williams ◽

Oluwapelumi O. Ojuri

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Hydraulic Conductivity ◽

Linear Regression ◽

Multiple Linear Regression ◽

Coarse Grained ◽

Soil Types ◽

Wide Range ◽

Artificial Neural ◽

Input Variables

AbstractAs a result of heterogeneity nature of soils and variation in its hydraulic conductivity over several orders of magnitude for various soil types from fine-grained to coarse-grained soils, predictive methods to estimate hydraulic conductivity of soils from properties considered more easily obtainable have now been given an appropriate consideration. This study evaluates the performance of artificial neural network (ANN) being one of the popular computational intelligence techniques in predicting hydraulic conductivity of wide range of soil types and compared with the traditional multiple linear regression (MLR). ANN and MLR models were developed using six input variables. Results revealed that only three input variables were statistically significant in MLR model development. Performance evaluations of the developed models using determination coefficient and mean square error show that the prediction capability of ANN is far better than MLR. In addition, comparative study with available existing models shows that the developed ANN and MLR in this study performed relatively better.

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Development of a Hybrid Artificial Neural Network and Genetic Algorithm Model for Regime Identification of Slurry Transport in Pipelines

Chemical Product and Process Modeling ◽

10.2202/1934-2659.1343 ◽

2009 ◽

Vol 4 (1) ◽

Cited By ~ 2

Author(s):

Sandip K Lahiri ◽

Kartik Chandra Ghanta

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Artificial Neural Network ◽

Operating Conditions ◽

Misclassification Error ◽

Wide Range ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Pipeline Design ◽

Hybrid Artificial Neural Network

Four distinct regimes were found existent (namely sliding bed, saltation, heterogeneous suspension and homogeneous suspension) in slurry flow in pipeline depending upon the average velocity of flow. In the literature, few numbers of correlations has been proposed for identification of these regimes in slurry pipelines. Regime identification is important for slurry pipeline design as they are the prerequisite to apply different pressure drop correlation in different regime. However, available correlations fail to predict the regime over a wide range of conditions. Based on a databank of around 800 measurements collected from the open literature, a method has been proposed to identify the regime using artificial neural network (ANN) modeling. The method incorporates hybrid artificial neural network and genetic algorithm technique (ANN-GA) for efficient tuning of ANN meta parameters. Statistical analysis showed that the proposed method has an average misclassification error of 0.03%. A comparison with selected correlations in the literature showed that the developed ANN-GA method noticeably improved prediction of regime over a wide range of operating conditions, physical properties, and pipe diameters.

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