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Memory efficient BFGS neural-network learning algorithms using MLP-network: a survey

Asirvadam , Vijanth Sagayan and McLoone, Sean and Irwin, George W (2004) Memory efficient BFGS neural-network learning algorithms using MLP-network: a survey. In: IEEE International Conference on Control Applications, 2004., 2-4 September 2004, Taiwan.

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Official URL: http://ieeexplore.ieee.org/iel5/9527/30192/0138727...

Abstract

This paper surveys various implementation of a memory efficient second order (Broyden, Fletcher, Goldfard and Shanno) BFGS training algorithms which includes novel optimal memory (OM) BFGS neural network training algorithm, proposed by the present authors, which optimises performance in relation to available memory. Simulation results using a control benchmark problems show that OM BFGS, which is mathematically equivalent to full memory (FM) BFGS training when there are no constraints on memory, have performance gain compared to other memory efficient BFGS training algorithms.

Item Type:Conference or Workshop Item (Paper)
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Q Science > QA Mathematics > QA76 Computer software
Departments / MOR / COE:Departments > Electrical & Electronic Engineering
ID Code:4029
Deposited By: Dr Vijanth Sagayan Asirvadam
Deposited On:18 Jan 2011 01:39
Last Modified:18 Jan 2011 01:39

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