Neural-Based Separating Method for Nonlinear Mixtures

A neural-based method for source separation in nonlinear mixture is proposed in this paper. A cost function, which consists of the mutual information and partial moments of the outputs of the separation system, is defined to extract the independent signals from their nonlinear mixtures. A learning algorithm for the parametric RBF network is established by using the stochastic gradient descent method. This approach is characterized by high learning convergence rate of weights, modular structure, as well as feasible hardware implementation. Simulation results demonstrated the success of our proposed method in this paper. Key words: Blind signal separation, nonlinear mixture, RBF networks, statistical independence, cost function.

• Type: Conference
• Year: 2007
• Where: ISNN
• Authors: Ying Tan
• Keywords: Cost Function, ISNN 2007, Nonlinear Mixture, Rbf Networks