Independent Component Analysis and Signal Separation
DOI: 10.1007/978-3-540-74494-8_33
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An Evolutionary Approach for Blind Inversion of Wiener Systems

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Cited by 5 publications
(4 citation statements)
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“…These techniques include least squares minimization [15], simulated annealing [19], genetic algorithm [24], and artificial neural networks [28,29]. In the context of real-time processing, the work in [14] presents an efficient online technique that processes the input sequence one frame at a time.…”
Section: Introductionmentioning
confidence: 99%
“…These techniques include least squares minimization [15], simulated annealing [19], genetic algorithm [24], and artificial neural networks [28,29]. In the context of real-time processing, the work in [14] presents an efficient online technique that processes the input sequence one frame at a time.…”
Section: Introductionmentioning
confidence: 99%
“…Then, the algorithm proposed in [11] is based on gradient descent search, and as a result, is inherently vulnerable to risk of premature convergence to a local minimum. On the other hand, we also found that the technique developed by Rojas et al [16], although it is more robust in terms of the potential for local convergence consider that the original source s(n) is a non-Gaussian i.i.d. process, which makes exploring a limited set of signals.…”
Section: Proposalmentioning
confidence: 56%
“…Also, as an alternative to gradient-based algorithms, Rojas et al [16] employ a genetic algorithm (GA), in order to perform the search task for the optimal values of the parameters. The nonlinear part of the Hammerstein system may be approximated by n-th order odd polynomials.…”
Section: 11mentioning
confidence: 99%
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