Successive Generation of AR-2 Basin Associated with Nonlinear Signals

Abstract

A method is presented for clustering signal space generated by information intensive decentralized systems. By identifying signal space with locally square summable functions, a universal dynamics is designed for successively approximating feature parameters in Fourier images. The approximation sequence is not monotone. However, we can design aggregation scheme for ordering Fourier images governed by feature parameters to be detected. The clustering rule was verified through simulation studies.