Echo　state　network　(ESN)　refers　to　a　popular　recurrent　neural　network　with　a　largely　and　randomly　generated　reservoir　for　its　rapid　learning　ability.　However,　it　is　difficult　to　design　a　reservoir　that　matches　a　specific　task.　To　solve　the　structure　design　of　the　reservoir,　a　pseudo-inverse　decompositionbased　self-organizing　modular　echo　state　(PDSM-ESN)　is　proposed.　PDSM-ESN　is　constructed　by　growing-pruning　method,　where　the　error　and　condition　number　are　used,　respectively.　Since　the　self-organizing　process　may　negatively　affect　the　learning　speed,　the　pseudo-inverse　decomposition　is　adopted　to　improve　learning　speed,　which　means　the　output　weights　are　learned　by　an　iterative　incremental　method.　Meanwhile,　to　solve　the　ill-posed　problem,　the　modular　sub-reservoirs　corresponding　to　the　high　condition　number　are　pruned.　Simulation　results　indicate　that　PDSM-ESN　has　better　prediction　performance　and　run-time　complexity　compared　with　the　traditional　ESN　models.　(c)　2021　Elsevier　B.V.　All　rights　reserved.