Prediction of noisy chaotic time series using an optimal radial basis function neural network

2001 ◽  
Vol 12 (5) ◽  
pp. 1163-1172 ◽  
Author(s):  
Henry Leung ◽  
Titus Lo ◽  
Sichun Wang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Radial Basis Function ◽  
Basis Function ◽  
Chaotic Time Series ◽  
Radial Basis

The Research Based on RBF Neural Network in the Power of Prediction of Grain Depot

2012 ◽  
Vol 182-183 ◽  
pp. 1358-1361
Author(s):  
Le Xiao ◽  
Min Peng Hu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Electricity Consumption ◽  
Nonlinear Time Series ◽  
Modeling And Prediction ◽  
Radial Basis

According to the fact that the use of electricity in grain depot is nonlinear time series, the article introduces the prediction model of electricity based on Radial Basis Function Neural Network, and conducts the modeling and prediction by adopting the historical electricity consumption of a typical grain depot. As the result of simulation shows, the model obtains better forecasting results in grain depot electricity.

scholarly journals Using Evolving ANN-Based Algorithm Models for Accurate Meteorological Forecasting Applications in Vietnam

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Tim Chen ◽  
N. Kapron ◽  
J. C.-Y. Chen
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Radial Basis Function ◽  
Basis Function ◽  
Time Variation ◽  
Tidal Waves ◽  
Radial Basis ◽  
Physically Based ◽  
Artificial Neural Network Ann ◽  
Meteorological Tsunamis

The reproduction of meteorological tsunamis utilizing physically based hydrodynamic models is complicated in light of the fact that it requires large amounts of information, for example, for modelling the limits of hydrological and water driven time arrangement, stream geometry, and balanced coefficients. Accordingly, an artificial neural network (ANN) strategy utilizing a backpropagation neural network (BPNN) and a radial basis function neural network (RBFNN) is perceived as a viable option for modelling and forecasting the maximum peak and variation with time of meteorological tsunamis in the Mekong estuary in Vietnam. The parameters, including both the nearby climatic weights and the wind field factors, for finding the most extreme meteorological waves, are first examined, through the preparation of evolved neural systems. The time series of meteorological tsunamis were used for training and testing the models, and data for three cyclones were used for model prediction. Given the 22 selected meteorological tidal waves, the exact constants for the Mekong estuary, acquired through relapse investigation, are A = 9.5 × 10−3 and B = 31 × 10−3. Results showed that both the Multilayer Perceptron Network (MLP) and evolved radial basis function (ERBF) methods are capable of predicting the time variation of meteorological tsunamis, and the best topologies of the MLP and ERBF are I3H8O1 and I3H10O1, respectively. The proposed advanced ANN time series model is anything but difficult to use, utilizing display and prediction tools for simulating the time variation of meteorological tsunamis.

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