An Uncertainty Oriented Grade Estimation Method Based on Fuzzy Wavelet Neural Network
Grade estimation is one of the most complicated aspects in mining. Its complexity originates from scientific uncertainty. In this paper, a fuzzy wavelet neural network (FWNN) is proposed for grade estimation. This fuzzy neural network uses wavelet basis function as membership function whose shape can be adjusted on line so that the networks have better learning and adaptive ability. The new FWNN method combing the properties of the fuzzy computing and the advantages of wavelet neural networks provide fast and reliable ore grade estimation, with minimum assumptions and minimum requirements for modeling skills. The FWNN grade estimation method has been tested on a number of real deposits. The result shows that the FWNN has advantages of rapid training, generality and accuracy grade estimation approach. It can provide with a very fast and robust alternative to the existing time-consuming methodologies for ore grade estimation.