Abstract
This paper presents the study results of complex-valued non-linear system identification problems in digital communication area using a sequential learning algorithm referred to as Complex Minimal Resource Allocation Network (CMRAN) algorithm. This CMRAN is an extension to the recently developed MRAN algorithm that can be used for online learning and has the ability to grow and prune the complex RBF network's hidden neurons ensuring a compact network structure. Simulation results presented suggest that CMRAN is very effective in modeling nonlinear systems, with performance achieved often being superior to that of some existing methods.
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