کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
562981 | 875462 | 2014 | 11 صفحه PDF | دانلود رایگان |
• We develop an immune-inspired framework for performing ICA over finite fields.
• The framework addresses the problem as a population of distinct solutions that represent each extraction vector.
• The proposal is implemented with the state-of-the-art cob-aiNet[C] algorithm.
• The simulation results reveal that the method is competitive for lower-dimensional scenarios, while it also handles larger instances.
In this work, we present a novel bioinspired framework for performing ICA over finite (Galois) fields of prime order P. The proposal is based on a state-of-the-art immune-inspired algorithm, the cob-aiNet[C], which is employed to solve a combinatorial optimization problem — associated with a minimal entropy configuration — adopting a Michigan-like population structure. The simulation results reveal that the strategy is capable of reaching a performance similar to that of standard methods for lower-dimensional instances with the advantage of also handling scenarios with an elevated number of sources.
Journal: Signal Processing - Volume 96, Part B, March 2014, Pages 153–163