Aiming　at　the　structure　design　of　an　echo　state　network　(ESN),　an　incremental　regularized　echo　state　network　(IRESN)　is　proposed　in　this　paper.　The　reservoir　of　the　IRESN　is　composed　of　independent　sub-reservoir　modular　networks.　Firstly,　the　sub-reservoirs　are　obtained　using　the　singular　value　decomposition　method,　and　the　singular　values　of　the　weight　matrix　of　each　sub-reservoir　can　be　guaranteed　to　be　less　than　one.　Then,　depending　on　the　problem,　complexity　or　residual　error,　the　sub-reservoirs　are　added　to　the　network　one　after　another　until　the　preset　termination　conditions　are　met.　In　the　process　of　generating　the　IRESN,　the　echo　state　property　can　be　guaranteed　without　scaling　the　reservoir　weight　matrix.　Furthermore,　in　order　to　tackle　the　ill-posed　problem,　in　the　process　of　incremental　learning,　the　output　weights　are　trained　using　the　regularization　method,　and　the　leave-one-out　cross-validation　method　is　used　to　select　the　regularization　parameter.　The　simulation　results　show　that　the　IRESN　has　compact　structure　and　high　prediction　accuracy　compared　with　other　ESNs.　Copyright　©2022　Control　and　Decision.