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Radial Basis Function Neural Network based on Growing Neural Gas Network applied for evaluation of oil agglomeration process efficiency

Marcin Kamiński, Stanisławski Radosław, Bastrzyk Anna

Physicochemical Problems of Mineral Processing

2020

Vol. 56, iss. 6

194--205

In this study, the neural model for modeling of oil agglomeration of dolomite in the presence of anionic and cationic surfactants (sodium oleate and dodecylammonium hydrochloride) was implemented. The effect of surfactants concentration, oil dosage, time of mixing, pH, and mixing speed of the impeller in the process recovery were investigated using Radial Basis Function Neural Network (RBFNN). A significant problem in this modeling, was the selection of the structure of the neural network. In algorithms based on the RBFNN, the issue mentioned relates to the number of nodes in the determination of the hidden layer. Also, the distribution of functions in data space is significant. In the proposed solution, at this stage of the neural model design, the Growing Neural Gas Network (GNGN) was implemented. Such a procedure introduced automation of the calculation process. The centers were obtained from the GNGN and the structure (number of radial neurons) can be approximated based on a simple searching algorithm. The idea of the data calculations was implemented as an original algorithm that can be easily transferred to Matlab, Python, or Octave software. The values predicted from the neural networks model were in good agreement with the experimental data. Thus, the RBFNN-GNGN model used in this study, can be employed as a reliable and accurate method to predict, and in the future to optimize the performance of oil agglomeration process.

Rainfall time series forecasting based on Modular RBF Neural Network model coupled with SSA and PLS

Wu Yu J., Yu J.

Journal of Theoretical and Applied Computer Science

2012

Vol. 6, nr 2

3-12

Accurate forecast of rainfall has been one of the most important issues in hydrological research. Due to rainfall forecasting involves a rather complex nonlinear data pattern; there are lots of novel forecasting approaches to improve the forecasting accuracy. In this paper, a new approach using the Modular Radial Basis Function Neural Network (M–RBF–NN) technique is presented to improve rainfall forecasting performance coupled with appropriate data–preprocessing techniques by Singular Spectrum Analysis (SSA) and Partial Least Square (PLS) regression. In the process of modular modeling, SSA is applied for the time series extraction of complex trends and structure finding. In the second stage, the data set is divided into different training sets by Bagging and Boosting technology. In the third stage, the modular RBF–NN predictors are produced by a different kernel function. In the fourth stage, PLS technology is used to choose the appropriate number of neural network ensemble members. In the final stage, least squares support vector regression is used for ensemble of the M–RBF–NN to prediction purpose. The developed RBF–NN model is being applied for real time rainfall forecasting and flood management in Liuzhou, Guangxi. Aimed at providing forecasts in a near real time schedule, different network types were tested with the same input information. Additionally, forecasts by M–RBF–NN model were compared to the convenient approach. Results show that the predictions made using the M–RBF–NN approach are consistently better than those obtained using the other method presented in this study in terms of the same measurements. Sensitivity analysis indicated that the proposed M-RBF-NN technique provides a promising alternative to rainfall prediction.