scholarly journals An adaptive RBF network optimised using a genetic algorithm applied to rainfall forecasting

2005 ◽  
Author(s):  
C. Jareanpon ◽  
W. Pensuwon ◽  
R.J. Frank ◽  
N. Davey
Keyword(s):  
Genetic Algorithm ◽  
Rainfall Forecasting ◽  
Rbf Network

RBF Network Based on Improved Niche Hybrid Hierarchy Genetic Algorithm

2012 ◽  
Vol 433-440 ◽  
pp. 775-780
Author(s):  
Fang Wang ◽  
Jin Lan Yu ◽  
Pin Chang Zhu ◽  
Xi Feng Wei
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Chaotic Time Series ◽  
Premature Convergence ◽  
Rbf Network ◽  
Operation Speed ◽  
System Resource ◽  
Simulation Results

The improved niche hybrid hierarchy genetic algorithm is presented to overcome the premature convergence which happens in genetic algorithm constructing RBF network. The niche with poor fitness of every individual is eliminated to save system resource and raise operation speed. The simulation results demonstrate the better predicted performance on the Mackey-Glass chaotic time series than other algorithms.

HYBRID OF GENETIC ALGORITHM AND SIMULATED ANNEALING FOR SUPPORT VECTOR REGRESSION OPTIMIZATION IN RAINFALL FORECASTING

2013 ◽  
Vol 12 (02) ◽  
pp. 1350012 ◽  
Author(s):  
CHANGMING ZHU ◽  
JIANSHENG WU
Keyword(s):  
Genetic Algorithm ◽  
Kernel Function ◽  
Subset Selection ◽  
Feature Subset Selection ◽  
Support Vector ◽  
Feature Subset ◽  
Parameter Setting ◽  
Rainfall Forecasting ◽  
Input Feature ◽  
Kernel Parameter

Accurate forecasting of rainfall has been one of the most important issues in hydrological research such as river training works and design of flood warning systems. Support vector regression (SVR) is a popular regression method in rainfall forecasting. Type of kernel function and kernel parameter setting in the SVR traing procedure, along with the input feature subset selection, significantly influence regression accuracy. In this paper, an effective hybrid optimization strategy by combining the strengths of genetic algorithm (GA) and simulated annealing (SA), is employed to simultaneously optimize the input feature subset selection, the type of kernel function and the kernel parameter setting of SVR, namely GASA–SVR. The developed GASA–SVR model is being applied for monthly rainfall forecasting in Guilin of Guangxi. The GA is carried out as a main frame of this hybrid algorithm while SA is used as a local search strategy to help GA jump out of local optima and avoid sinking into the local optimal solution early. Compared with SVR, pure GA–SVR and HGA–SVR, results show that the hybrid GASA–SVR model can correctly select the discriminating input features subset, successfully identify the optimal type of kernel function and all the optimal values of the parameters of SVR with the lowest prediction error values in rainfall forecasting, can also significantly improve the rainfall forecasting accuracy. Experimental results reveal that the predictions using the proposed approach are consistently better than those obtained using the other methods presented in this study in terms of the same measurements. Those results show that the proposed GASA–SVR model provides a promising alternative to monthly rainfall prediction.

Adaptive Genetic Algorithm for Multilayer RBF Network and its Application on Real Function Approximation

2013 ◽  
Vol 380-384 ◽  
pp. 1166-1169 ◽  
Author(s):  
Yan Hui Wang ◽  
Kun Zhang
Keyword(s):  
Genetic Algorithm ◽  
Computer Simulation ◽  
Real Function ◽  
Function Approximation ◽  
Clustering Algorithm ◽  
Least Squares Method ◽  
Adaptive Genetic Algorithm ◽  
Rbf Networks ◽  
Rbf Network ◽  
Hidden Layer

With the application of adaptive genetic algorithm to the training of multi-layer RBF networks and the optimization of the hidden layer centers and width values and using regularized least squares method, weight vectors is obtained. Computer simulation shows that the precision of real function approximation by this algorithm is much higher than the precision by clustering algorithm for multi-layer RBF networks.

Research of Fuzzy RBF Approach Method Based on IMGA

2015 ◽  
Vol 719-720 ◽  
pp. 311-315
Author(s):  
Yan Fen Luo
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Inference ◽  
Original System ◽  
Immune Memory ◽  
Speed Of Convergence ◽  
Rbf Network ◽  
Approach Method

A research of fuzzy RBF approach method based on IMGA is proposed, depending on the equivalency between RBF network and fuzzy inference, a fuzzy RBF network is designed. At the same time, the parameters and weights of the fuzzy RBF are optimized based on the immune memory genetic algorithm (IMGA), and the speed of convergence is accelerated. The optimized system is simulated by MATLAB, and compared with the original system, the approach effect of the fuzzy RBF is improved by IMGA.

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