Fundamentals of digital image processing
Fundamentals of digital image processing
Multilayer feedforward networks are universal approximators
Neural Networks
Expert Systems with Applications: An International Journal
Mutual complement between statistical and neural network approaches for rock magnetism data analysis
Expert Systems with Applications: An International Journal
A multilayer perceptron-based medical decision support system for heart disease diagnosis
Expert Systems with Applications: An International Journal
Two-dimensional cubic convolution
IEEE Transactions on Image Processing
Training feedforward networks with the Marquardt algorithm
IEEE Transactions on Neural Networks
Simulating wheat yield in New South Wales of Australia using interpolation and neural networks
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
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The inverse problem of magnetic petrophysics is to determine magnetic contents of rocks/ores provided with their susceptibility readings already known. This has not been studied yet due to its unknown applications. This paper proposes a novel application of solving this inverse problem for instant estimation of iron-ore grade in mining. This application is based on numerical simulation using neural networks assisted with 2D interpolation for determining the magnetite and hematite contents through known magnetic susceptibility data. This study shows that a four-layer multilayer perceptron (MLP) trained properly is able to accurately simulate the magnetic contents of iron-ores, which can lead to instant estimation of iron-ore grade in situ in iron-ore mining.