Nonlinear Signal Separation for Multinonlinearity Constrained Mixing Model  

Oleh:

Gao, P ; Woo, W. L. ; Dlay, S. S.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 17 no. 3 (May 2006), page 796-802.
Topik: non linear; non linear; signal separation; multinonlinearity; constrained; mixing model

Ketersediaan

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

Lihat Detail Induk

Isi artikel

In this letter, a new type of non linear mixture is derived and developed into a multinonlinearity constrained mixing model. The proposed signal separation solution integrates the Theory of Series Reversion with a polynomial neural network whereby the hidden neurons are spanned by a set of mutually reversed activation functions. Simulations have been undertaken to support the theory of the proposed scheme and the results indicate promising performance.

Opini Anda

Klik untuk menuliskan opini Anda tentang koleksi ini!

Kembali