The optimal multi-layer structure of backpropagation networks

Authors:
Songyot Sureerattanan;Huynh Ngoc Phien;Nidapan Sureerattanan;Nikos E. Mastorakis
Affiliations:
Information Technology Department, Bank of Thailand, Bangkok, Thailand;Information Technology Department, Bank of Thailand, Bangkok, Thailand;Information Technology Department, Bank of Thailand, Bangkok, Thailand;Information Technology Department, Bank of Thailand, Bangkok, Thailand
Venue:
NN'06 Proceedings of the 7th WSEAS International Conference on Neural Networks
Year:
2006

Citing 7
Cited 4

Multilayer feedforward networks are universal approximators

Neural Networks
Learning internal representations by error propagation

Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Back-propagation algorithm which varies the number of hidden units

Neural Networks
Neural network system for forecasting method selection

Decision Support Systems
Neurofuzzy adaptive modelling and control

Neurofuzzy adaptive modelling and control
Artificial Neural Networks: Theory and Applications

Artificial Neural Networks: Theory and Applications
Time Series Analysis, Forecasting and Control

Time Series Analysis, Forecasting and Control

Aerosol size distribution using sun-photometer AOD data of five wavelengths and artificial neural network

WSEAS TRANSACTIONS on SYSTEMS
Applications of neural networks in continuous casting

WSEAS TRANSACTIONS on SYSTEMS
Model of homeostatic artificial neuron

NN'10/EC'10/FS'10 Proceedings of the 11th WSEAS international conference on nural networks and 11th WSEAS international conference on evolutionary computing and 11th WSEAS international conference on Fuzzy systems
Neural networks for detecting cyclic behavior in autocorrelated process

Computers and Industrial Engineering

Quantified Score

Hi-index	0.00

Visualization

Abstract

A novel algorithm obtained by using Bayesian information criterion (BIC) is presented to systematically choose the optimal multilayer network structure, via the number of hidden layers and hidden nodes of each layer, of backpropagation (BP) networks. Simulation results with daily data on stock prices in the Thai market show that the algorithm performs satisfactorily. Moreover, the proposed algorithm is also compared to Daqi-Shouyi method.