Wu Huixin
Department of Information Engineering, North China University of Water Resources and Electric Power, 450046, Zhengzhou, China
Mo Duo
Department of Information Engineering, North China University of Water Resources and Electric Power, 450046, Zhengzhou, China
Liu Ran
School of software, North China University of Water Resources and Electric Power, 450011, Zhengzhou, China
ABSTRACT
Mineral resource prediction is the basis for many spatial operations in digital mine. Thus, there is a growing interest in constructing effective prediction algorithm, both in applications and in science. In this study, a new nonlinear combination forecasting algorithm based on adaptive neural network, support vector machine and relevance vector machine is presented to overcome the limitation in linear combination forecasting. Furthermore, a new method of selecting weight coefficient is proposed based on rough set theory. Theoretical analysis and forecasting examples all show that the new techniques has reinforcement learning properties and universalized capabilities. Finally, future trends for research and development in this area are highlighted.
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How to cite this article
Wu Huixin, Mo Duo and Liu Ran, 2013. A New Nonlinear Combination Forecasting Algorithm and its Application in Digital Mine. Information Technology Journal, 12: 8066-8073.
DOI: 10.3923/itj.2013.8066.8073
URL: https://scialert.net/abstract/?doi=itj.2013.8066.8073
DOI: 10.3923/itj.2013.8066.8073
URL: https://scialert.net/abstract/?doi=itj.2013.8066.8073
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