A New Model of Self-Organizing Neural Networks and Its Application in Data Projection  

Oleh:

Su, Mu-Chun ; Chang, Hsiao-Te

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 12 no. 1 (2001), page 153-157.
Topik: networks; self - organizing; networks; application; data projection

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.5 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

In this paper a new model of self - organizing neural networks is proposed. An algorithm called “double self - organizing feature map” (DSOM) algorithm is developed to train the novel model. By the DSOM algorithm the network will adaptively adjust its network structure during the learning phase so as to make neurons responding to similar stimulus have similar weight vectors and spatially move nearer to each other at the same time. The final network structure allows us to visualize high - dimensional data as a two dimensional scatter plot. The resulting representations allow a straightforward analysis of the inherent structure of clusters within the input data. One high - dimensional data set is used to test the effectiveness of the proposed neural networks.

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