|کد مقاله||کد نشریه||سال انتشار||مقاله انگلیسی||ترجمه فارسی||نسخه تمام متن|
|407128||678129||2016||10 صفحه PDF||سفارش دهید||دانلود رایگان|
• A long-term forecasting of oil production was done using MLMVN.
• Univariate and multivariate forecasting models were developed.
• MLMVN is efficient for both multivariate and univariate forecasting models.
• MLMVN-based prediction combines both regression and pattern recognition approaches.
In this paper, we discuss the long-term time series forecasting using a Multilayer Neural Network with Multi-Valued Neurons (MLMVN). This is a complex-valued neural network with a derivative-free backpropagation learning algorithm. We evaluate the proposed approach using a real-world data set describing the dynamic behavior of an oilfield asset located in the coastal swamps of the Gulf of Mexico. We show that MLMVN can be efficiently applied to univariate and multivariate one-step- and multi-step ahead prediction of reservoir dynamics. This paper is not only intended for proposing to use a complex-valued neural network for forecasting, but to deeper study some important aspects of the application of ANN models to time series forecasting that could be of the particular interest for pattern recognition community.
Journal: Neurocomputing - Volume 175, Part B, 29 January 2016, Pages 980–989