Parameterized online quasi-Newton training for high-nonlinearity function approximation using multilayer neural networks

2011 ◽  
Author(s):  
Hiroshi Ninomiya
Keyword(s):  
Neural Networks ◽  
Function Approximation ◽  
High Nonlinearity ◽  
Multilayer Neural Networks ◽  
Quasi Newton

A MATLAB-Based Study on Approximation Performances of Improved Algorithms of Typical BP Neural Networks

2013 ◽  
Vol 313-314 ◽  
pp. 1353-1356 ◽  
Author(s):  
Shuo Ding ◽  
Qing Hui Wu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Adaptive Learning ◽  
Function Approximation ◽  
Nonlinear Function ◽  
Bp Neural Networks ◽  
Advantages And Disadvantages ◽  
The Best Approximation ◽  
Bp Networks ◽  
Quasi Newton

BP neural networks are widely used and the algorithms are various. This paper studies the advantages and disadvantages of improved algorithms of five typical BP networks, based on artificial neural network theories. First, the learning processes of improved algorithms of the five typical BP networks are elaborated on mathematically. Then a specific network is designed on the platform of MATLAB 7.0 to conduct approximation test for a given nonlinear function. At last, a comparison is made between the training speeds and memory consumption of the five BP networks. The simulation results indicate that for small scaled and medium scaled networks, LM optimization algorithm has the best approximation ability, followed by Quasi-Newton algorithm, conjugate gradient method, resilient BP algorithm, adaptive learning rate algorithm. Keywords: BP neural network; Improved algorithm; Function approximation; MATLAB

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