Nonlinear blind source separation using a hybrid RBF-FMLP network

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Nonlinear blind source separation using a hybrid RBF-FMLP network

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A novel scheme for blind source separation of nonlinearly mixed signals is developed using a hybrid system based on radial basis function (RBF) and feedforward multilayer perceptron (FMLP) networks. In this paper, the development of the proposed RBF-FMLP network is discussed, which hinges on the theory of nonlinear regularisation. The proposed network uses simultaneously local and global mapping bases to perform both signal separation and reconstruction of continuous signals in addition to signals that exhibit a high degree of fluctuation. The parameters of the proposed system are estimated jointly using the generalised gradient descent approach thereby rendering the training process relatively simple and efficient in computation. Simulations of both synthetic and speech signals have been undertaken to verify the efficacy of the proposed scheme in terms of speed, accuracy and robustness against noise.

Inspec keywords: multilayer perceptrons; radial basis function networks; blind source separation

Other keywords: radial basis function networks; local mapping bases; nonlinear regularisation; continuous signals; fluctuation; synthetic signals; feedforward multilayer perceptron networks; hybrid RBF-FMLP network; generalised gradient descent approach; training process; global mapping bases; nonlinearly mixed signals; nonlinear blind source separation; speech signals

Subjects: Digital signal processing; Neural nets (theory); Neural computing techniques; Signal processing theory; Signal processing and detection

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