A comparison between single layer and multilayer artificial neural networks in predicting diesel fuel properties using near infrared spectrum

Al-kaf, Hasan Ali Gamal; Chia, Kim Seng; Alduais, Nayef Abdulwahab Mohammed

doi:10.1080/10916466.2018.1425717

Cited by 22 publications

(19 citation statements)

References 13 publications

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“…To benchmark their findings, a single hidden layer feed-forward neural network structure ( Figure 9) was used to develop maize-grain spatial yield prediction models. Network topology was limited to a single hidden layer because of reportedly a faster execution and comparable prediction performance [44][45][46].…”

Section: Feed-forward Neural Networkmentioning

confidence: 99%

Integration of Precision Farming Data and Spatial Statistical Modelling to Interpret Field-Scale Maize Productivity

et al. 2019

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Spatial variability in soil, crop, and topographic features, combined with temporal variability between seasons can result in variable annual yield patterns within a paddock. The complexity of interactions between yield-limiting factors such as soil nutrients and soil water require specialist statistical processing to be able to quantify variability, and thus inform crop management practices. This study uses multiple linear regression models, Cubist regression and feed-forward neural networks to predict spatial maize-grain (Zea mays) yield at two sites in the Waikato Region, New Zealand. The variables considered were: crop reflectance data from satellite imagery, soil electrical conductivity, soil organic matter, elevation, rainfall, temperature, solar radiation, and seeding density. This exercise explores methods which may be useful in predicting yield from proximal and remote sensed data with higher resolution than traditional low spatial resolution point sampling using soil testing and yield response curves.

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Section: Feed-forward Neural Networkmentioning

confidence: 99%

Integration of Precision Farming Data and Spatial Statistical Modelling to Interpret Field-Scale Maize Productivity

et al. 2019

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“…Nowadays, near-infrared spectroscopy (NIRS) technology showed increasing number of applications in various fields such as medical, chemical, and food analysis [1][2][3]. Applications that based on NIRS are developed to overcome several factors in the conventional methods which are time-consuming, destructive, and cost-effective.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network and partial least square in predicting blood hemoglobin using near-infrared spectrum

Idrus

Chia

2019

IJEECS

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<p>Predictive models is crucial in near-infrared (NIR) spectroscopic analysis. Partial least square - artificial neural network (PLS-ANN) is a hybrid method that may improve the performance of prediction in NIR spectroscopic analysis. This study investigates the advantage of PLS-ANN over the well-known modelling in spectroscopy analysis that is partial least square (PLS) and artificial neural network (ANN). The results show that ANN that coupled with first order SG derivatives achieved the best prediction with root mean square error of prediction (RMSEP) of 0.3517 gd/L and coefficient of determination ( ) of 0.9849 followed by PLS-ANN with RMSEP of 0.4368 gd/L and of 0.9787, and PLS with RMSEP of 0.4669 gd/L and of 0.9727. This suggests that the spectrum information may unable to be totally represented by the first few latent variables of PLS and a nonlinear model is crucial to model these nonlinear information in NIR spectroscopic analysis.</p>

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“…Gang-Feng Li et al identified the adulterations and geographical origins of Chinese herbs by NIR spectroscopy and chemometrics [18]. Many traditional learning methods including Support Vector Machine (SVM) [19], Back Propagation Neural Network (BPNN) [20], Random Forest (RF) [21], etc., are combined with the NIR spectra to discriminate the geographical origin and quality of food and herbs [22]. However, compared with the NIR spectra of the above organics, there is more redundant information and noise in the NIR spectra of coal samples.…”

Section: Introductionmentioning

confidence: 99%

Identification of Coal Geographical Origin Using Near Infrared Sensor Based on Broad Learning

Lei

Rao

et al. 2019

Applied Sciences

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Geographical origin, an important indicator of the chemical composition and quality grading, is one essential factor that should be taken into account in evaluating coal quality. However, traditional coal origin identification methods based on chemistry experiments are not only time consuming and labour intensive, but also costly. Near-Infrared (NIR) spectroscopy is an effective and efficient way to measure the chemical compositions of samples and has demonstrated excellent performance in various fields of quantitative and qualitative research. In this study, we employ NIR spectroscopy to identify coal origin. Considering the fact that the NIR spectra of coal samples always contain a large amount of redundant information and the number of samples is small, the broad learning algorithm is utilized here as the modelling system to classify the coal geographical origin. In addition, the particle swarm optimization algorithm is introduced to improve the structure of the Broad Learning (BL) model. We compare the improved model with the other five multivariate classification methods on a dataset with 243 coal samples collected from five countries. The experimental results indicate that the improved BL model can achieve the highest overall accuracy of 97.05%. The results obtained in this study suggest that the NIR technique combined with machine learning methods has significant potential for further development of coal geographical origin identification systems.

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A comparison between single layer and multilayer artificial neural networks in predicting diesel fuel properties using near infrared spectrum

Cited by 22 publications

References 13 publications

Integration of Precision Farming Data and Spatial Statistical Modelling to Interpret Field-Scale Maize Productivity

Integration of Precision Farming Data and Spatial Statistical Modelling to Interpret Field-Scale Maize Productivity

Artificial neural network and partial least square in predicting blood hemoglobin using near-infrared spectrum

Identification of Coal Geographical Origin Using Near Infrared Sensor Based on Broad Learning

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