Algorithms for clustering data
Algorithms for clustering data
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Artificial Intelligence: Structures and Strategies for Complex Problem Solving
Artificial Intelligence: Structures and Strategies for Complex Problem Solving
Evolutionary training of hardware realizable multilayer perceptrons
Neural Computing and Applications
Modelling electrical conductivity of groundwater using an adaptive neuro-fuzzy inference system
Computers & Geosciences
Fuzzy Modeling and Control
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
A hybrid computing scheme for shape optimisation in thermo-fluid problems
International Journal of Computational Intelligence Studies
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Mineral resources are a formal quantification of naturally occurring materials. Estimation of resource parameters such as grade and thickness may be carried out using different methodologies. In this paper, a soft methodology, which is artificial neural network (ANN) based fuzzy modelling is presented for grade estimation and its stages are demonstrated. The neuro-fuzzy method uses preliminary clustering and finally estimates the ore grades based on radial basis neural network and interpolation. Two case studies designed for both simulated and real data sets indicate that the approach is relatively accurate and flexible. In addition, the method is suitable for modelling via limited number of data. The results and performance comparisons with conventional methods show that the computing method is efficient.