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Nonlinear signal separation for multinonlinearity constrained mixing model

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3 Author(s)
Gao, P. ; Sch. of Electr., Univ. of Newcastle upon Tyne ; Woo, W.L. ; Dlay, S.S.

In this letter, a new type of nonlinear 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

Published in:

Neural Networks, IEEE Transactions on  (Volume:17 ,  Issue: 3 )