Introduction: Neural networks as associative devices

Braspenning, Peter J.

doi:10.1007/bfb0027020

Cited by 15 publications

(21 citation statements)

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“…Class discrimination processes occurred in the hidden layer and the synapses between the layers were estimated by an activation function. We used a logistic function and training rate of 0.20, previously applied to land cover classification ( Hepner et al, 1990 ; Richards, 1999 ; Braspenning & Thuijsman, 1995 ). Learning occurs by adjusting the weights in the node to minimize the difference between the output node activation, and BPNN then calculates the error at each iteration with root mean square (RMS) error.…”

Section: Methodsmentioning

confidence: 99%

PredictingPinus monophyllaforest cover in the Baja California Desert by remote sensing

Escobar-Flores

López-Sánchez

Sandoval

et al. 2018

PeerJ

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The Californian single-leaf pinyon (Pinus monophylla var. californiarum), a subspecies of the single-leaf pinyon (the world’s only one-needled pine), inhabits semi-arid zones of the Mojave Desert (southern Nevada and southeastern California, US) and also of northern Baja California (Mexico). This tree is distributed as a relict subspecies, at elevations of between 1,010 and 1,631 m in the geographically isolated arid Sierra La Asamblea, an area characterized by mean annual precipitation levels of between 184 and 288 mm. The aim of this research was (i) to estimate the distribution of P. monophylla var. californiarum in Sierra La Asamblea by using Sentinel-2 images, and (ii) to test and describe the relationship between the distribution of P. monophylla and five topographic and 18 climate variables. We hypothesized that (i) Sentinel-2 images can be used to predict the P. monophylla distribution in the study site due to the finer resolution (×3) and greater number of bands (×2) relative to Landsat-8 data, which is publically available free of charge and has been demonstrated to be useful for estimating forest cover, and (ii) the topographical variables aspect, ruggedness and slope are particularly important because they represent important microhabitat factors that can determine the sites where conifers can become established and persist. An atmospherically corrected a 12-bit Sentinel-2A MSI image with 10 spectral bands in the visible, near infrared, and short-wave infrared light region was used in combination with the normalized differential vegetation index (NDVI). Supervised classification of this image was carried out using a backpropagation-type artificial neural network algorithm. Stepwise multiple linear binominal logistical regression and Random Forest classification including cross validation were used to model the associations between presence/absence of P. monophylla and the five topographical and 18 climate variables. Using supervised classification of Sentinel-2 satellite images, we estimated that P. monophylla covers 6,653 ± 319 ha in the isolated Sierra La Asamblea. The NDVI was one of the variables that contributed most to the prediction and clearly separated the forest cover (NDVI > 0.35) from the other vegetation cover (NDVI < 0.20). Ruggedness was the most influential environmental predictor variable, indicating that the probability of occurrence of P. monophylla was greater than 50% when the degree of ruggedness terrain ruggedness index was greater than 17.5 m. The probability of occurrence of the species decreased when the mean temperature in the warmest month increased from 23.5 to 25.2 °C. Ruggedness is known to create microclimates and provides shade that minimizes evapotranspiration from pines in desert environments. Identification of the P. monophylla stands in Sierra La Asamblea as the most southern populations represents an opportunity for research on climatic tolerance and community responses to climate variability and change.

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Section: Methodsmentioning

confidence: 99%

PredictingPinus monophyllaforest cover in the Baja California Desert by remote sensing

Escobar-Flores

López-Sánchez

Sandoval

et al. 2018

PeerJ

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“…Artificial Neural Networks are inspired from a biological brain conceptual model to solve complex problems. 87 Consider how a human brain distinguishes between different people: every human has a similar overall structure ( e.g. , two eyes, two ears, one nose, etc.…”

Section: Ann For Modeling For Metal Adsorptionmentioning

confidence: 99%

“…(5) Optimization is achieved by reducing the error between observed and model-predicted responses by varying neurons in hidden layers, transfer functions, training algorithms and iterative modification of weights assigned to links emerging from the input layer. 87,89 Steps 5 and 6 (of Fig. 2) are repeated until further weight changes do not reduce errors (refer to S5.2†).…”

Section: Ann For Modeling For Metal Adsorptionmentioning

confidence: 99%

Application of neural network in metal adsorption using biomaterials (BMs): a review

Nighojkar

Zimmermann

Ateia

et al. 2023

Environ. Sci.: Adv.

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“…The best NN regression model that satisfies the criteria mentioned above will be selected for estimation and comparison purposes on the test dataset (X test ). More details about the NN can be found in [74]. 12 International Journal of Energy Research 2.5.…”

Section: Neural Network (Nn)mentioning

confidence: 99%

Multiobjective Optimization and Machine Learning Algorithms for Forecasting the 3E Performance of a Concentrated Photovoltaic-Thermoelectric System

Alghamdi

Maduabuchi

Yusuf³

et al. 2023

International Journal of Energy Research

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Previous theoretical research efforts which were validated by experimental findings demonstrated the thermo-economic benefits of the hybrid concentrated photovoltaic-thermoelectric (CPV-TE) system over the stand-alone CPV. However, the operating conditions and TE material properties for maximum CPV-TE performance may differ from those required in a standalone thermoelectric module (TEM). For instance, a high-performing TEM requires TE materials with high Seebeck coefficients and electrical conductivities, and at the same time, low thermal conductivities ( k ). Although it is difficult to attain these ideal conditions without complex material engineering, the low k implies a high thermal resistance and temperature difference across the TEM which raises the PV backplate’s temperature in a hybrid CPV-TE operation. The increased PV temperature may reduce the overall system’s thermodynamic performance. To understand this phenomenon, a study is needed to guide researchers in choosing the best TE material for an optimal operation of a CPV-TE system. However, no prior research effort has been made to this effect. One method of finding the optimum TE material property is to parametrically vary one or more transport parameters until an optimum point is determined. However, this method is time-consuming and inefficient since a global optimum may not be found, especially when large incremental step sizes are used. This study provides a better way to solve this problem by using a multiobjective optimization genetic algorithm (MOGA) which is fast and reliable and ensures that the global optimum is obtained. After the optimization has been conducted, the best performing conditions for maximum CPV-TE energy, exergy, and environmental (3E) performance are selected using the technique for order performance by similarity to ideal solution (TOPSIS) decision algorithm. Finally, the optimization workflow is deployed for 7000 test cases generated from 10 features using the optimal machine learning (ML) algorithm. The result of the optimization chosen by the TOPSIS decision-making method generated an output power, exergy efficiency, and CO2 saving of 44.6 W, 18.3%, and 0.17 g/day, respectively. Furthermore, among other ML algorithms, the Gaussian process regression was the most accurate in learning the CPV-TE performance dataset, although it required more computational effort than some algorithms like the linear regression model.

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Introduction: Neural networks as associative devices

Cited by 15 publications

References 0 publications

PredictingPinus monophyllaforest cover in the Baja California Desert by remote sensing

PredictingPinus monophyllaforest cover in the Baja California Desert by remote sensing

Application of neural network in metal adsorption using biomaterials (BMs): a review

Multiobjective Optimization and Machine Learning Algorithms for Forecasting the 3E Performance of a Concentrated Photovoltaic-Thermoelectric System

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