Classification

Masters, Timothy

doi:10.1016/b978-0-08-051433-8.50007-0

Cited by 16 publications

(21 citation statements)

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“…Principal component analysis can also be used to combine information from many input variables by converting multidimensional data to a new set of statistically uncorrelated components which account for most of the variance in the original data. However, Masters (1993) warns that, although the first few principal components may explain the majority of the variation in the data, the information that is important for the classification may reside in the last principal component.…”

Section: Discussionmentioning

confidence: 99%

“…This is necessary because the learning algorithm minimises the total sum of squares error over all the patterns in the training set. If deposit patterns were represented in the training set in the same proportion as they appear in the total data population, the learning algorithm would optimise the performance for non‐deposit patterns ( Masters 1993). The network would not learn to recognise the rare deposit patterns at all or would perform very poorly for this class of patterns.…”

Section: Methodsmentioning

confidence: 99%

“…A multilayer perceptron (MLP) neural network was chosen for this study because it performs very well for a large variety of problem types and has powerful function‐approximation capabilities ( Hornik 1991; Masters 1993). The network used in this study is shown in Figure 1.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

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Artificial neural networks: A new method for mineral prospectivity mapping

Brown

Gedeon

Groves

et al. 2000

Australian Journal of Earth Sciences

270

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A multilayer feed-forward neural network, trained with a gradient descent, back-propagation algorithm, is used to estimate the favourability for gold deposits using a raster GIS database for the Tenterfield 1:100 000 sheet area, New South Wales. The database consists of solid geology, regional faults, airborne magnetic and gamma-ray survey data (U, Th, K and total count channels), and 63 deposit and occurrence locations. Input to the neural network consists of feature vectors formed by combining the values from co-registered grid cells in each GIS thematic layer. The network was trained using binary target values to indicate the presence or absence of deposits. Although the neural network was trained as a binary classifier, output values for the trained network are in the range [0.1, 0.9] and are interpreted to indicate the degree of similarity of each input vector to a composite of all the deposit vectors used in training. These values are rescaled to produce a multiclass prospectivity map. To validate and assess the effectiveness of the neural-network method, mineral-prospectivity maps are also prepared using the empirical weights of evidence and the conceptual fuzzy-logic methods. The neural-network method produces a geologically plausible mineral-prospectivity map similar, but superior, to the fuzzy logic and weights of evidence maps. The results of this study indicate that the use of neural networks for the integration of large multisource datasets used in regional mineral exploration, and for prediction of mineral prospectivity, offers several advantages over existing methods. These include the ability of neural networks to: (i) respond to critical combinations of parameters rather than increase the estimated prospectivity in response to each individual favourable parameter; (ii) combine datasets without the loss of information inherent in existing methods; and (iii) produce results that are relatively unaffected by redundant data, spurious data and data containing multiple populations. Statistical measures of map quality indicate that the neural-network method performs as well as, or better than, existing methods while using approximately one-third less data than the weights of evidence method.KEY WORDS: fuzzy logic, GIS, mineral exploration, neural networks, weights of evidence. 764W. M. Brown et al. Figure 6 Prospectivity maps for the Tenterfield 1:100 000 area. (a) Weights of evidence method. (b) Fuzzy-logic method. (c) Neural network method. x, primary gold deposits and occurrences; ᭹, alluvial gold deposits and occurrences; ᮀ, undifferentiated gold deposits and occurrences.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Artificial neural networks: A new method for mineral prospectivity mapping

Brown

Gedeon

Groves

et al. 2000

Australian Journal of Earth Sciences

270

View full text Add to dashboard Cite

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“…It involves two stages: firstly, a feedforward stage where the exterior input information on the input nodes is propagated forward in order to compute the output information indicators at the output unit, and secondly, a backward phase where alterations to the connection weights are adjusted based on the differences between the computed and the actual indications at the output units [26,27]. …”

Section: Methodsmentioning

confidence: 99%

Electronic Nose Based on Independent Component Analysis Combined with Partial Least Squares and Artificial Neural Networks for Wine Prediction

Aguilera

Lozano

Paredes

et al. 2012

Sensors

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The aim of this work is to propose an alternative way for wine classification and prediction based on an electronic nose (e-nose) combined with Independent Component Analysis (ICA) as a dimensionality reduction technique, Partial Least Squares (PLS) to predict sensorial descriptors and Artificial Neural Networks (ANNs) for classification purpose. A total of 26 wines from different regions, varieties and elaboration processes have been analyzed with an e-nose and tasted by a sensory panel. Successful results have been obtained in most cases for prediction and classification.

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“…In spite of these difficulties, a significant number of successful applications of ANNs for dynamic modeling are reported over a wide spectrum of fields. ,,, Especially in the process engineering area, ANNs have been extensively used as nonlinear autoregressive exogenous (NARX) models for dynamic system identification of both univariate (single output) ,− and multivariate (multi-output) problems. ,,,− In the literature, multivariate systems are usually approximated either using a multi-output ANN model or an ensemble of single-output ANNs models where, in the latter case, a set of independent single-output ANN models, each approximating one output as a function of the inputs, is built.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Surrogate Modeling for Multistep-ahead Prediction of Multivariate Nonlinear Chemical Processes

Shokry

Baraldi

Zio

et al. 2020

Ind. Eng. Chem. Res.

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This work proposes a methodology for multivariate dynamic modeling and multistep-ahead prediction of nonlinear systems using surrogate models for the application to nonlinear chemical processes. The methodology provides a systematic and robust procedure for the development of data-driven dynamic models capable of predicting the process outputs over long time horizons. It is based on using surrogate models to construct several Nonlinear AutoRegressive eXogenous models (NARX), each one approximating the future behavior of one process output as a function of the current and previous process inputs and outputs. The developed dynamic models are employed in a recursive schema to predict the process future outputs over several time steps (multistep-ahead prediction). The methodology is able to manage two different scenarios: 1) one in which a set of input-output signals collected from the process is only available for training, and 2) another in which a mathematical model of the process is available and can be used to generate specific datasets for training. With respect to the latter, the proposed methodology includes a specific procedure for the selection of training data in dynamic modeling based on Design Of Computer Experiment (DOCE) techniques. The proposed methodology is applied to case studies from the process industry presented in the literature. The results show very high prediction accuracies over long time horizons. Also, thanks to the flexibility, robustness and computational efficiency of surrogate modeling, the methodology allows dealing with a wide range of situations, which would be difficult to address using first principle models.

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Classification

Cited by 16 publications

References 0 publications

Artificial neural networks: A new method for mineral prospectivity mapping

Artificial neural networks: A new method for mineral prospectivity mapping

Electronic Nose Based on Independent Component Analysis Combined with Partial Least Squares and Artificial Neural Networks for Wine Prediction

Dynamic Surrogate Modeling for Multistep-ahead Prediction of Multivariate Nonlinear Chemical Processes

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