Title :
Regression Analysis Using Modular Structured Neural Network
Author :
Mitani, Yasunori
Author_Institution :
Dept. of Electron. & Robot Eng., Fukuyama Univ., Fukuyama, Japan
Abstract :
Paying attention to realistic systems in the actual engineering fields, we must very often treat their systems as stochastic systems with non-Gaussian, nonlinear and/or non-stationary properties. In this paper, a regression analysis method for such stochastic systems is proposed by introducing reasonably a modular structured neural network. The proposed modular structured neural network is constructed by the hierarchical combination of each expert neural network for analyzing the regression relationship between input and output signals in each local stationary section, and a neural network for the prediction of weights contained in the above expert neural network. The effectiveness of the proposed method is experimentally confirmed by applying it to the simulation and actual road traffic noise data.
Keywords :
neural nets; regression analysis; signal processing; stochastic processes; expert neural network; local stationary section; modular structured neural network; nonGaussian property; nonlinear property; nonstationary property; regression analysis; regression relationship; road traffic noise data; stochastic systems; Neural networks; Noise; Regression analysis; Roads; Stochastic processes; Stochastic systems; System identification; local stationary process; neural network; regression analysis; stochastic system identification;
Conference_Titel :
Computing and Networking (CANDAR), 2013 First International Symposium on
Conference_Location :
Matsuyama
Print_ISBN :
978-1-4799-2795-1
DOI :
10.1109/CANDAR.2013.45