scholarly journals Adaptive Output Tracking of Unknown MIMO Nonlinear Systems Using Radial Basis Function Neural Networks

1997 ◽  
Vol 40 (1) ◽  
pp. 52-59 ◽  
Author(s):  
Teh-Lu LIAO ◽  
Chi-Ming CHIU
Keyword(s):  
Neural Networks ◽  
Nonlinear Systems ◽  
Radial Basis Function ◽  
Basis Function ◽  
Mimo Nonlinear Systems ◽  
Output Tracking ◽  
Radial Basis

scholarly journals Random vibration analysis with radial basis function neural networks

2021 ◽  
Author(s):  
Xi Wang ◽  
Jun Jiang ◽  
Ling Hong ◽  
Jian-Qiao Sun
Keyword(s):  
Neural Networks ◽  
Nonlinear Systems ◽  
Probability Distribution ◽  
Radial Basis Function ◽  
Basis Function ◽  
Random Waves ◽  
Random Vibrations ◽  
Transient Responses ◽  
Earthquake Excitation ◽  
Radial Basis

AbstractRandom vibrations occur in many engineering systems including buildings subject to earthquake excitation, vehicles traveling on a rough road and off-shore platform in random waves. Analysis of random vibrations for linear systems has been well studied. For nonlinear systems, particularly for multi-degree-of-freedom systems, however, there are still many challenges including analyzing the probability distribution of transient responses of the system. Monte Carlo simulation was considered the only viable method for this task. In this paper, We propose a method to construct semi-analytical transient solutions of the probability distribution of transient responses of nonlinear systems by using the radial basis function neural networks. The activation functions consist of normalized Gaussian probability density functions. Two examples are presented to show the effectiveness of the proposed solution method. The transient probability distributions and response moments of these examples are presented, which have not been reported in the literature before.

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