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A Hybrid Self-organizing Neural Gas Based Network

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 ：

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A Hybrid Self-organizing Neural Gas Based Network

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  1. A Hybrid Self-organizing Neural Gas Based Network James Graham, Janusz A. Starzyk IJCNN, 2008 Presented by Hung-Yi Cai 2010/10/06

  2. Outlines • Motivation • Objectives • Methodology • Experiments • Conclusions • Comments

  3. 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

  4. 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.

  5. Methodology In this paper, to propose a hybrid method by altering GNG algorithm and combining the concept of SOM.

  6. Growing Neural Gas The presentation of the GNG algorithm.

  7. The New Hybrid Algorithm The presentation of the hybrid algorithm.

  8. Experiments To analyze the performance of the proposed algorithm we tested against the performance of GNG algorithm.

  9. Conclusions • The hybrid algorithm retains most of the advantages of the GNGwhile 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.

  10. Comments • Advantages • Reduce number of parameter • More biologically plausible design • Applications • Neural Network • SOM

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