The　proportionate　normalized　least　mean　square　(PNLMS)　algorithm　is　used　in　sparse　system　identification　for　its　simplicity　and　adaptively　step-size　adjusting　scheme.　However,　the　PNLMS　has　the　noisy　input　and　the　non-Gaussian　output　noise　problem.　A　bias　Compensated　PNLMF　algorithm　(called　BCPNLMF)　for　identifying　sparse　system　has　been　proposed　to　solve　aforementioned　issues.　The　BCPNLMF　algorithm　which　takes　advantage　of　the　bias　compensated　and　the　proportionate　scheme,　can　achieve　better　steady-state　accuracy　and　faster　convergence　speed　besides　identify　the　system　parameters　in　noisy　input　and　output　with　non-Gaussian　character　environments.　Simulation　results　carried　out　in　sparse　system　identification　confirm　the　remarkable　performance　of　the　BCPNLMF,　compared　with　other　well-known　algorithms.　©　2022　SPIE