Discrete-time signal processing
Discrete-time signal processing
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Feature Extraction Based on ICA for Binary Classification Problems
IEEE Transactions on Knowledge and Data Engineering
Face Recognition Using IPCA-ICA Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bacteria Foraging Based Independent Component Analysis
ICCIMA '07 Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007) - Volume 02
A hybrid least square-fuzzy bacterial foraging strategy for harmonic estimation
IEEE Transactions on Evolutionary Computation
Learning multiview face subspaces and facial pose estimation using independent component analysis
IEEE Transactions on Image Processing
Algorithms for nonnegative independent component analysis
IEEE Transactions on Neural Networks
FPGA implementation of ICA algorithm for blind signal separation and adaptive noise canceling
IEEE Transactions on Neural Networks
GBF Trained Neuro-fuzzy Equalizer for Time Varying Channels
International Journal of Applied Evolutionary Computation
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Independent component analysis (ICA) technique separates mixed signals blindly without any information of the mixing system. Fast ICA is the most popular gradient based ICA algorithm. Bacterial foraging optimization based ICA (BFOICA) and constrained genetic algorithm based ICA (CGAICA) are two recently developed derivative free evolutionary computational ICA techniques. In BFOICA the foraging behavior of E. coli bacteria present in our intestine is mimicked for evaluation of independent components (IC) where as in CGAICA genetic algorithm is used for IC estimation in a constrained manner. The present work evaluates the error performance of fast ICA, BFOICA and CGAICA algorithms when they are implemented with finite length register. Simulation study is carried on both fixed and floating point ICA algorithms. It is observed that the word length greatly influences the separation performance. A comparison of fixed-point error performance of the three algorithms is also carried out in this work.