A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation

Authors:
Pejman Tahmasebi;Ardeshir Hezarkhani
Affiliations:
Department of Mining, Metallurgy and Petroleum Engineering, Amirkabir University of Technology (Tehran Polytechnic), Hafez Ave. No. 24, Hafez ave., Tehran, Iran;Department of Mining, Metallurgy and Petroleum Engineering, Amirkabir University of Technology (Tehran Polytechnic), Hafez Ave. No. 24, Hafez ave., Tehran, Iran
Venue:
Computers & Geosciences
Year:
2012

Citing 8
Cited 0

Multilayer feedforward networks are universal approximators

Neural Networks
Structure optimization of fuzzy neural network by genetic algorithm

Fuzzy Sets and Systems - Special issue on fuzzy neural control
Toward global optimization of neural networks: a comparison of the genetic algorithm and backpropagation

Decision Support Systems
HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems

Neural Networks
Data-driven fuzzy analysis in quantitative mineral resource assessment

Computers & Geosciences
A novel approach for ANFIS modelling based on full factorial design

Applied Soft Computing
Determining the saliency of feature measurements obtained from images of sedimentary organic matter for use in its classification

Computers & Geosciences
The semi-automated classification of sedimentary organic matter in palynological preparations

Computers & Geosciences

Quantified Score

Hi-index	0.00

Visualization

Abstract

The grade estimation is a quite important and money/time-consuming stage in a mine project, which is considered as a challenge for the geologists and mining engineers due to the structural complexities in mineral ore deposits. To overcome this problem, several artificial intelligence techniques such as Artificial Neural Networks (ANN) and Fuzzy Logic (FL) have recently been employed with various architectures and properties. However, due to the constraints of both methods, they yield the desired results only under the specific circumstances. As an example, one major problem in FL is the difficulty of constructing the membership functions (MFs).Other problems such as architecture and local minima could also be located in ANN designing. Therefore, a new methodology is presented in this paper for grade estimation. This method which is based on ANN and FL is called ''Coactive Neuro-Fuzzy Inference System'' (CANFIS) which combines two approaches, ANN and FL. The combination of these two artificial intelligence approaches is achieved via the verbal and numerical power of intelligent systems. To improve the performance of this system, a Genetic Algorithm (GA) - as a well-known technique to solve the complex optimization problems - is also employed to optimize the network parameters including learning rate, momentum of the network and the number of MFs for each input. A comparison of these techniques (ANN, Adaptive Neuro-Fuzzy Inference System or ANFIS) with this new method (CANFIS-GA) is also carried out through a case study in Sungun copper deposit, located in East-Azerbaijan, Iran. The results show that CANFIS-GA could be a faster and more accurate alternative to the existing time-consuming methodologies for ore grade estimation and that is, therefore, suggested to be applied for grade estimation in similar problems.