Noisy time series prediction using M-estimator based robust radial basis function neural networks with growing and pruning techniques

2009 ◽  
Vol 36 (3) ◽  
pp. 4717-4724 ◽  
Author(s):  
Chien-Cheng Lee ◽  
Yu-Chun Chiang ◽  
Cheng-Yuan Shih ◽  
Chun-Li Tsai
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Radial Basis Function ◽  
Basis Function ◽  
Time Series Prediction ◽  
M Estimator ◽  
Noisy Time Series ◽  
Radial Basis ◽  
Pruning Techniques

scholarly journals An Adaptive Learning Rate for RBFNN Using Time-Domain Feedback Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Syed Saad Azhar Ali ◽  
Muhammad Moinuddin ◽  
Kamran Raza ◽  
Syed Hasan Adil
Keyword(s):  
Neural Networks ◽  
Radial Basis Function ◽  
Adaptive Learning ◽  
Basis Function ◽  
Learning Algorithm ◽  
Time Series Prediction ◽  
Learning Rate ◽  
Theoretical Development ◽  
Feedback Analysis ◽  
Radial Basis

Radial basis function neural networks are used in a variety of applications such as pattern recognition, nonlinear identification, control and time series prediction. In this paper, the learning algorithm of radial basis function neural networks is analyzed in a feedback structure. The robustness of the learning algorithm is discussed in the presence of uncertainties that might be due to noisy perturbations at the input or to modeling mismatch. An intelligent adaptation rule is developed for the learning rate of RBFNN which gives faster convergence via an estimate of error energy while giving guarantee to thel2stability governed by the upper bounding via small gain theorem. Simulation results are presented to support our theoretical development.

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