Aerodynamic Parameter Estimation Using Two-Stage Radial Basis Function Neural Network

2017 ◽  
Author(s):  
Dhan Jeet Singh ◽  
Nischal K Verma ◽  
A K Ghosh ◽  
Jitu Sanwale ◽  
Appasaheb Malagaudanavar
Keyword(s):  
Neural Network ◽  
Parameter Estimation ◽  
Radial Basis Function ◽  
Basis Function ◽  
Two Stage ◽  
Aerodynamic Parameter ◽  
Radial Basis

Forecasting the HS300 Stock Index with Radial Basis Function Neural Network Using Two-Stage Particle Swarm Optimization

2013 ◽  
Vol 709 ◽  
pp. 632-637
Author(s):  
Xiao Bo Zhang
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Radial Basis Function ◽  
Basis Function ◽  
Fitness Function ◽  
Particle Swarm ◽  
Stock Index ◽  
Two Stage ◽  
Swarm Optimization ◽  
Radial Basis

This paper introduces a hybrid radial basis function neural network (RBFNN) using a two-stage particle swarm optimization. In stage one, we initially demonstrate how the PSO learning method simultaneously determines the optimal number of hidden neurons, centres and widths of the radial basis function. Afterwards we apply the least mean squares (LMS) method to calculate the weight between the hidden layer and output layer in stage two. Then we propose a novel fitness function and make forecast simulation of HS 300 stock index. It proves to improve the performance of RBFNN.

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