A study of conjugate gradient recurrent network on time series problems

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A study of conjugate gradient recurrent network on time series problems

 

Author: Ma, Wing-hong
Title: A study of conjugate gradient recurrent network on time series problems
Degree: M.Sc.
Year: 1997
Subject: Time-series analysis
Neural networks (Computer science)
Real-time control
Hong Kong Polytechnic University -- Dissertations
Department: Multi-disciplinary Studies
Pages: vi, 36 leaves : ill. ; 30 cm
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b1398642
URI: http://theses.lib.polyu.edu.hk/handle/200/2250
Abstract: Recurrent networks, which include feedback loops, are capable of processing temporal patterns and accepting sequences as inputs and producing them as outputs. Recurrent networks can be trained with error backpropagation and real-time recurrent learning. But these training algorithms require many training cycles for the networks to converge. Learning algorithm based on second order method such as conjugate gradient had been successfully applied to feedforward and recurrent networks and the number of epochs needed for training was significantly reduced. However, the conjugate gradient method can only be run in batch mode; this mades it unsuitable for time series problems. In this dissertation, a modified conjugate gradient recurrent learning algorithm, which is able to run in sequence mode, is proposed to tackle the time series problems. The Henon series was used to test the performance of the network and the results were compared with the modified steepest gradient recurrent learning algorithm, a first order recurrent learning algorithm. Finally, the results were also compared with varies form of RTRL. Results shows that modified CGRL network can learn the Henon series successfully. It also had performance advantages over modified SGRL and varies form of RTRL in terms number of epochs required for convergence.

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