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A Hybrid Self-organizing Neural Gas Based Network. James Graham, Janusz A. Starzyk IJCNN, 2008 Presented by Hung-Yi Cai 2010/10/06. Outlines. Motivation Objectives Methodology Experiments Conclusions Comments. Motivation. The Growing Neural Gas algorithm ： - PowerPoint PPT Presentation
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Intelligent Database Systems Lab
國立雲林科技大學National Yunlin University of Science and Technology
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A Hybrid Self-organizing Neural Gas Based Network
James Graham, Janusz A. StarzykIJCNN, 2008
Presented by Hung-Yi Cai2010/10/06
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
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Outlines· Motivation· Objectives· Methodology· Experiments· Conclusions· Comments
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
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Motivation· The Growing Neural Gas algorithm：
─ has parameters that are constant in time─ since it is incremental, there is no need to determine the
number of nodes in priori.
· However, GNG has some disadvantages：─ must be set before the implementation of several
variables ─ aren't particularly supportive of biologically based neuron
learning
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
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Objectives· GNG was examined and altered into what is believed
to be a more biologically plausible design.
· To propose a form of hybrid of the standard SOM and GNG networks.
· This is accomplished by taking the general structure of the SOM and adding properties of the neural gas network.
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Methodology· In this paper, to propose a hybrid method by
altering GNG algorithm and combining the concept of SOM.
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Growing Neural Gas
· The presentation of the GNG algorithm.
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.The New Hybrid Algorithm
· The presentation of the hybrid algorithm.
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Experiments· To analyze the performance of the proposed
algorithm we tested against the performance of GNG algorithm.
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
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Conclusions· The hybrid algorithm retains most of the
advantages of the GNG while adapting a reduced number of parameters and more biologically plausible design.
· While the hybrid algorithm performs admirably in terms of the quality of results when compared to GNG algorithm, it is slower and an actual quantifiable comparison has yet to be performed.
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
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Comments· Advantages
─ Reduce number of parameter─ More biologically plausible design
· Applications─ Neural Network─ SOM
