Geometric Neural Computing  

Oleh:

Bayro-Corrochano, E. J.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 12 no. 5 (2001), page 968-986.
Topik: geometric data; geometric; neural computing

Ketersediaan

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

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Isi artikel

This paper shows the analysis and design of feedforward neural networks using the coordinate - free system of Clifford or geometric algebra. It is shown that real-, complex -, and quaternion - valued neural networks are simply particular cases of the geometric algebra multidimensional neural networks and that some of them can also be generated using support multivector machines (SMVM s). Particularly, the generation of radial basis function for neurocomputing in geometric algebra is easier using the SMVM, which allows one to find automatically the optimal parameters. The use of support vector machines in the geometric algebra framework expands its sphere of applicability for multi dimensional learning. Interesting examples of non linear problems show the effect of the use of an adequate Clifford geometric algebra which alleviate the training of neural networks and that of SMVM s.

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